2.0 -//Pentabarf//Schedule//EN PUBLISH FJP8J9@@talks.staging.osgeo.org -FJP8J9 OGC APIs, an introduction with GeoServer en en 20251103T090000 20251103T120000 3.00000 OGC APIs, an introduction with GeoServer GeoServer is a web service for publishing your geospatial data using industry standards for vector, raster and mapping. It powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage and disseminate data at scale. This workshop will get your started with OGC APIs, and their implementation in GeoServer, covering: * An introduction to the common concepts in OGC APIs * The landing page of a service * The OpenAPI definition of a service * OGC API Features, the schemaless, vector data access service * OGC API Styles, or how to access styles and eventually apply them client side * OGC API Tiles, or how to get tiled data and images * OGC API Map, or how to get maps in a given area and projection * OGC API Coverages, downloading raw raster data * OGC API Processes, processing data on the fly GeoSolutions will make available an all-in-one package to run the workshop as a VM for VirtualBox or a self-contained Zip file for Windows. This should be pre-installed on attendees laptops before the workshop. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/FJP8J9/ Reston A Andrea Aime Simone Giannecchini PUBLISH 9BPSD7@@talks.staging.osgeo.org -9BPSD7 GRASS Addon Development with Python en en 20251103T130000 20251103T160000 3.00000 GRASS Addon Development with Python GRASS works as a powerful geospatial processing engine that works on a small laptop to a huge supercomputer. GRASS also makes it easy to move from using a graphical user interface (GUI) to command line interface or Python API. During this workshop we will develop a GRASS addon exploring the various GRASS Python modules, tooling, and best practice required to produce high quality open source software. The bonus material for this workshop will also cover tools written in C. To get the most out of this workshop, basic Python and GIS experience is recommended. The workshop will use an online environment, so no software installation on laptops is required from participants. However, emailing the workshop presenters ahead of time to set up the software locally is certainly allowed. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/9BPSD7/ Reston A Corey White PUBLISH HTSKNB@@talks.staging.osgeo.org -HTSKNB MapStore, Development of an Extension en en 20251103T090000 20251103T120000 3.00000 MapStore, Development of an Extension This workshop will provide an introduction to building your own Extensions for the MapStore Open Source framework, an highly modular Open Source WebGIS framework to create, manage and securely share maps and geospatial applications. An Extension is a plugin component that can be distributed as a separate package (a zip file), and be installed, activated and used at runtime. Creating an extension allows a developer to add custom components and functionalities to the map viewer. The workshop provides an overview of the MapStore technological stack and introduces to the development of a MapStore extension. The topics covered during the workshop are the following: - Introduction to MapStore - How to setup the development environment for a MapStore Extension - Development of an Extension - Installation of an Extension inside a MapStore context map viewer PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/HTSKNB/ Reston B Tobia Di Pisa Stefano Bovio PUBLISH W3SF3Y@@talks.staging.osgeo.org -W3SF3Y Broadband Data QuickStart en en 20251103T130000 20251103T160000 3.00000 Broadband Data QuickStart Broadband availability mapping has long been a critical but complex component of policy, regulatory oversight, and research. In this tutorial we assume that a person will have a passing familiarity with datasets and python, but it is not required to successfully participate in the session. We will begin with a quick walk-through history and previous efforts to understand the availability of broadband around the US. This will lead us into a discussion about the current state of broadband mapping through the Broadband Data Collection (BDC). We will conclude this section with discussions about longitudinal analysis that is possible using both current and historical datasets. Using online Jupyter notebooks will allow participants to follow along in their own accounts on their own computers, and if a participant is uncomfortable with Python they will be able to participate in the discussions and process. We will begin to work with the BDC dataset that is collected and demonstrate best practices for working the data apart from the Broadband Fabric and with the Fabric. We will dive into methods around analyzing the data using completely open source and open data tools. Closing this section, we will move onto working with the proprietary datasets and discuss the licenses available to researchers and if you have public service and outreach activities in your program you will be prepared to engage with industry and the public. After we have worked through the current federal data sets we will move towards pulling in data sources including but not limited to MLAB, Ookla, Site location data, building footprints, and OpenAddresses. These correlative datasets will allow us to being ground truthing and quantifying errors in the BDC to enable a thorough understanding of accuracy, this can be used to scientifically quantify errors and used to potentially update the dataset to be more accurate. We will also show methods for working with the BDC and historic FCC Form 477 data which will enable 10-15 year longitudinal studies. Finally, we will review public and private fiber maps that are available and how to access them. At the conclusion of the tutorial, we will facilitate a discussion between all attendees about what kinds of analysis in which they are interested and how this data can be incorporated into their analysis going forward. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/W3SF3Y/ Reston B W. Nick Pappin PUBLISH RJG3KN@@talks.staging.osgeo.org -RJG3KN Tile serving with MapLibre/Martin/Planetiler - base and overlays en en 20251103T090000 20251103T120000 3.00000 Tile serving with MapLibre/Martin/Planetiler - base and overlays In this workshop we will generate base map tiles from OSM data using Planetiler, set up Martin tile server, set up nginx to serve our sample web site that will use MapLibre GL JS to show the map. Additionally (time permitting), we will add a PostgreSQL server, and will use osm2pgsql to import extra data from the same OSM dump, and do on-the-fly tile generation from PG. What topics do we plan to cover in your workshop? – * generating base maps * setting up postgres with data * generate overlay tiles on the fly * serving tiles * visualizing tiles with MapLibre * adding data layers Pre-requirements for attendees: * docker * docker compose See https://github.com/maplibre/workshop?tab=readme-ov-file#pre-reqs PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/RJG3KN/ Reston C Yuri Astrakhan PUBLISH 3RFHJL@@talks.staging.osgeo.org -3RFHJL Urban Digital Models with MapStore and Cesium en en 20251103T130000 20251103T160000 3.00000 Urban Digital Models with MapStore and Cesium In an era where urban development demands innovative solutions, this workshop describes processes and tools used by the author and his team to build and consume digital models for urban environments. Leveraging the latest advancements in open-source geospatial technology, we will guide attendees through the process of transforming vector data and point cloud datasets into 3D models using open-source tools in order to consume them within MapStore WebGIS framework (thanks to its support for Cesium JS) and consequently within the GeoNode platform. This hands-on session is designed around the implementation of pipelines and 3D visualization techniques, directly applicable to real-world urban contexts. Participants will gain exclusive insights into the development of 3D models, drawing upon our team's extensive experience with urban reconstruction projects. The workshop will showcase how these digital replicas serve as essential tools for urban planning, facilitating detailed analysis, scenario planning and public engagement. Attendees will be provided with sample data from a selected city, offering a unique opportunity to learn skills to convert and visualize this data on the web using 3D Tiles format within a MapStore instance. This process not only demonstrates the conversion of geospatial data into 3D Tiles format ready to be served over the web but also underscores the importance of interoperability and accessibility in geospatial data management. Through this workshop, participants will: - Learn how to effectively convert vector (shapefiles) and point cloud (las) data into 3DTiles format compatible with MapStore and Cesium JS. - Explore the MapStore WebGIS application for enhanced 3D visualization and GIS capabilities using the generated 3D Tiles. - Get access to a docker artifact containing the tools presented during the webinar with sample data to replicate the process - Engage in hands-on exercises that illustrate the creation and utilization of 3D digital models, from data conversion to visualization and analysis within a MapStore framework. This session is ideal for GIS professionals, urban planners, researchers, and anyone interested in the forefront of geospatial technology and urban development. A repository with a docker solution will be provided to the attendees to facilitate the setup of all needed tools. A basic understanding of python and javascript languages is needed even if most of the processes and interactions will be done with a dedicated user interface. Join us to explore how the synergy between MapStore, GeoNode and innovative 3D data processing can pave the way for smarter, more sustainable urban futures. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/3RFHJL/ Reston C Tobia Di Pisa Stefano Bovio PUBLISH DRGFFQ@@talks.staging.osgeo.org -DRGFFQ Cartography for Professional Quality Maps in QGIS en en 20251103T090000 20251103T120000 3.00000 Cartography for Professional Quality Maps in QGIS When you're flipping through a book or journal article, you probably look at the pictures first. In a presentation, you don’t read the text. You look at the pictures! It’s the same with websites. Because images draw our attention, they are an incredibly important tool for conveying the message of your text or presentation. But communicating clearly with maps requires a specific set of skills that is distinct from other forms of communication. Key Concepts: • Minimize. Keep only what's absolutely necessary. • Tell the Story. What do I want my reader to learn from this map? How does it support the claims I make in my text? What story should my map tell? • Communication. Does my map communicate well? In this workshop, we'll learn strategies and steps to take in making maps that not only look good but communicate well. We’ll learn approaches and guidelines for creating professional quality maps using QGIS and practice a workflow that can be applied to other graphical GIS programs or even non-map figures. This workshop is divided into two sections. The first covers concepts and approaches to designing maps. The second will provide hands-on experience making a map in QGIS. Participants are encouraged to bring examples from their own work for discussion. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/DRGFFQ/ Lake Fairfax A Michele Tobias PUBLISH B8HTWQ@@talks.staging.osgeo.org -B8HTWQ Vector tiles with GeoServer en en 20251103T130000 20251103T160000 3.00000 Vector tiles with GeoServer GeoServer is well known for its wide support for classic OGC services. But does it handle vector tiles? Yes, it does, but dealing with the assumptions of the XYZ ecosystem requires some simple preparations. The set up of the scale dependencies and data sources may also be new to those already used to vector tiles, while it comes more naturally to those having a OGC service background. Join this workshop to learn basics about vector tiles, their usage, the performance factors, how to set up GeoServer for painless usage, and how to use vector tiles with the tools in the XYZ ecosystem. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/B8HTWQ/ Lake Fairfax A Andrea Aime PUBLISH MTKUCT@@talks.staging.osgeo.org -MTKUCT Introduction to GeoNode, the open source geosptial CMS en en 20251103T090000 20251103T120000 3.00000 Introduction to GeoNode, the open source geosptial CMS GeoNode is an open source web platform for the development of interoperable spatial data infrastructures. The software is designed to be easily extended, customized and integrated into existing systems. The workshop will provide an introduction to GeoNode starting with an overview of its functionalities for managing data, users and documents covering also more advanced concepts like managing layers, editing layer styles, managing maps and geostories and more. The workshop will also cover advanced administration and configuration concepts covering, the administration panel, management commands, monitoring and analytics and much more. In order to participate no previous knowledge of GeoServer and OGC services is required, but a basic knowledge of GIS concepts and basic data formats (shapefiles, geotiff) is recommended. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/MTKUCT/ Lake Fairfax B Giovanni Allegri PUBLISH 7WKGKJ@@talks.staging.osgeo.org -7WKGKJ Environment setup and predictive modeling workshop en en 20251103T130000 20251103T160000 3.00000 Environment setup and predictive modeling workshop Remotely sensed data, newer technologies, and modeling frameworks are fundamentally changing the way in which we understand and manage resources. In this short course, we will discuss how to setup a data science processing environment using Conda, build that processing environment using basic user permissions, demonstrate how to acquire raster and vector data from STAC, OSM, and REST services, illustrate how to use those data to create a well spread and balanced sample, create a machine learning model, and finally depict spatially explicit estimates of mean canopy cover and modeling error, locally and within cloud services using opensource Jupyter Notebooks and Python. Our notebook and use case walks analysts through the basic steps needed to create a well spread and balanced sample, integrating field data with remotely sensed data using machine learning and Raster Tools, and further illustrate how analysts can standardize this type of workflow using open-source data streams and software. Workshop learning objectives include: 1) learning about environments, 2) learn multiple sample designs strategies, 3) learn how to access cloud-based data and services, 4) learn the advantages of a well spread and balanced sample, 5) create a predictive model, and 6) explore how to use those models to depict mean and standard error estimates in a spatially explicit manner. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/7WKGKJ/ Lake Fairfax B John Hogland PUBLISH NCM8WU@@talks.staging.osgeo.org -NCM8WU eoAPI: open-source cloud-native geospatial data cataloging and distribution en en 20251103T090000 20251103T120000 3.00000 eoAPI: open-source cloud-native geospatial data cataloging and distribution This interactive session will introduce you to eoAPI (https://eoapi.dev/) - a powerful cloud-native framework that simplifies access to Earth observation data. By the end of this workshop, you'll understand how to use eoAPI to catalog, discover, visualize, and analyze geospatial data efficiently. eoAPI combines pgSTAC(postgres), STAC-fastapi, Titiler, and TiPG open source projects into a single deployable catalog with API using infrastructure as code, either with Kubernetes (K8s) or AWS CDK. It’s engineered for serverless Cloud Hosting but can work on most servers or services. Objectives: - Learn about STAC (SpatioTemporal Asset Catalog) and its role in Earth observation - Explore the key components of eoAPI and how they work together - Gain hands-on experience working with STAC metadata, dynamic raster visualizations, and vector feature services - Explore how eoAPI can fit into your geospatial workflows PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/NCM8WU/ Regency Ballroom A Alex Mandel PUBLISH MN7NCT@@talks.staging.osgeo.org -MN7NCT Exploring Cloud-Native Geospatial Formats: Hands-on with Raster Data en en 20251103T130000 20251103T160000 3.00000 Exploring Cloud-Native Geospatial Formats: Hands-on with Raster Data Ever wonder what GDAL is doing under the hood when you read a GeoTIFF file? Doubly so when the file is a Cloud-optimized GeoTIFF (COG) on a remote server somewhere? Have you been wondering what this new Zarr thing is all about and how it actually works? Then there's the whole Kerchunk/VirtualiZarr indexing to get cloud-native access for non-cloud-native data formats, what's that about? Cloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools such as Dask to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud native data formats actually store data and what tools are doing to access it. This workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on raster data formats. We'll start on the surface by surveying the current cloud-native geospatial landscape to gain an understanding of why cloud native is important and how it is being used, including: * the core tenets of cloud-native geospatial data formats * cloud-native data formats for both raster and non-raster geospatial data * the intersection with SpatioTemporal Asset Catalogs (STAC) and how higher-level STAC-based tooling can leverage cloud-native formats for efficient raster data access processing of cloud-native data Then we'll get hands-on and go deep to build up an in-depth understanding of how cloud native raster formats work. We'll examine the COG format and read a COG from a cloud source by hand using just Python, progressively grabbing data from the image until we can extract a target tile, all without using any image libraries. We'll repeat the same exercise for geospatial data in Zarr format to see how that compares to our experience with COGs. Lastly we'll turn our attention to Kerchunk/VirtualiZarr to see how these technologies might allow us to better optimize data access with non-cloud-native formats. #### Prerequisites This workshops expects some familiarity with geospatial programming in Python. Most of the notebook code is already provided, so any gaps in understanding don't necessarily prohibit completing the exercises. That said, a basic knowledge of STAC and Cloud-Native Geospatial Python tooling and working with rasters as single and multidimensional arrays is quite helpful. A good primer workshop is Alex Leith of Auspatious's [Cloud-Native Geospatial for Earth Observation Workshop](https://github.com/auspatious/cloud-native-geospatial-eo-workshop). It is recommended to work through those activities or have an equivalent knowledge prior to working through the notebooks in this workshop. ### Pre-workshop Prep We'll have a lot to cover in the workshop and time is against us. Please try to come with a working notebook execution environment already setup and ready to go. The [workshop repository README](https://github.com/jkeifer/cng-raster-formats) outlines three different options: build and run the docker container, use a GitHub Codespace, or run from a python venv managed via `uv`. Due to the uncertain quality of conference internet, a local option (docker or using `uv`) is recommended, but Codespaces can be useful for those that cannot run either of those options. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/MN7NCT/ Regency Ballroom A Jarrett Keifer PUBLISH BGECPN@@talks.staging.osgeo.org -BGECPN Introduction to PostGIS en en 20251103T090000 20251103T120000 3.00000 Introduction to PostGIS PostGIS is the industry standard for spatial data storage and analysis. This workshop introduces attendees to the basics of GIS modelling and analysis using spatial SQL. Creating tables, inserting data, and most importantly, forming spatial queries to answer your spatial questions. The workshop assumes some minor familiarity with GIS and database concepts, but should be easy to follow by any attendee with basic experience in programming or scripting (creating a script or program and running it to get a result). PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/BGECPN/ Regency Ballroom B Paul Ramsey Leo Hsu PUBLISH C8RQMA@@talks.staging.osgeo.org -C8RQMA QGIS Model Designer en en 20251103T130000 20251103T160000 3.00000 QGIS Model Designer This 3 hour class will cover building Processing Models using the Model Designer. Students will build custom tools to make repeatable tasks easier. Students will build models during class and will modify those models to make them more functional. Topics Covered: Introduction Processing Tools Model Designer Building a Model Command Line Students will need to use the Current Long Term Release of QGIS. Students will also need a basic understanding of QGIS. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/C8RQMA/ Regency Ballroom B Randal Hale PUBLISH ASPSJW@@talks.staging.osgeo.org -ASPSJW OpenStreetMap 101 en en 20251103T090000 20251103T120000 3.00000 OpenStreetMap 101 OpenStreetMap offers free and open source geospatial data through the power of crowdsourced volunteer efforts. This workshop is designed for beginners, perhaps those who may have seen OpenStreetMap credited in the bottom right corner of a web map and are curious to learn more about it, or those who are interested in extracting OSM data for professional use. No prior experience with mapping or GIS is required for this session. Participants will gain a foundational knowledge of OpenStreetMap and its community of contributors, learn how to confidently make edits using the iD editor, explore different tools supported by OpenStreetMap US, learn how OSM’s open data supports various geospatial projects, and understand the communal nature of the project’s governance structure. The rough agenda outline is: - Introduction to OpenStreetMap and its community (30 minutes) - Getting started with iD editor (30 minutes) - Mapping together (either a group mapping activity, or guidance to choose personal mapping projects, depending on attendance) (45 minutes) - Exploring OSM US tools (30 minutes) - Open mapping and troubleshooting (45 minutes) “OpenStreetMap 101” will offer a comprehensive introduction to the world of collaborative mapping, empower participants to contribute meaningfully to OSM, and leverage its data to enrich the open geospatial landscape. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/ASPSJW/ Lake Audubon Alyssa Castronuovo Jake Low PUBLISH HCTCFT@@talks.staging.osgeo.org -HCTCFT Mergin Maps Admin Essentials en en 20251103T130000 20251103T160000 3.00000 Mergin Maps Admin Essentials Join us for the Mergin Maps workshop for GIS admins to empower you to master collaborative mapping in the field. Besides setting up and synchronizing your Mergin Maps project with QGIS and your mobile device, we will also equip you with the skills to manage your collaborative mapping projects effectively, including sharing, transferring, setting roles and permissions, and handling synchronization conflicts. The workshop places also a special emphasis on good practices. In the workshop you will also collect your spatial data and post-process them in QGIS together with other participants. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/HCTCFT/ Lake Audubon Peter Petrik Martin Dobias PUBLISH LTDBTU@@talks.staging.osgeo.org -LTDBTU Build a GraphRAG To Bring Geospatial Awareness to LLM Agents en en 20251103T090000 20251103T120000 3.00000 Build a GraphRAG To Bring Geospatial Awareness to LLM Agents Large Language Models (LLMs) have the potential to address pressing global challenges, including public health, economic development, and disaster resilience. However, to effectively utilize LLMs in these domains, they require access to comprehensive geoinformatics data that is integrated by location. This data will enable LLMs to provide near-real-time decision support for problems that vary based on local geographic contexts. To achieve this sustainably and at scale, feature-level metadata is essential. However, geospatial data is currently managed at the feature class level. For many years, we have treated data quality as an analytics problem, delegating dirty data to the data team for cleanup in the data warehouse or lake. This approach is not suitable for AI applications. GenAI applications operate in real time, making decisions on the fly. If the data is incorrect, incomplete, or poorly structured, AI will not rectify it. Instead, it will make erroneous decisions more rapidly. You can’t wait until the analytics layer to ensure data quality when AI agents need to reason, plan, and act in real-time. This builds upon the workshop given at FedGeoDay, 2025, teaching participants a hands-on approach that leverages open-source software for publishing feature-level metadata using the data mesh architecture pattern. We will build a spatial knowledge graph (SKG) from feature classes that represent semantic geospatial relationships across entire networks of features in multiple domains, which can be updated in real-time to understand the downstream impact or cumulative effect of events of interest. We will then publish the SKG as a GraphGRAG and learn to write prompts to teach an LLM Agent how to generate GeoSPARQL queries from natural language, and subsequently convert the query results into readable text. Finally, we will learn how we can display the features referenced in natural language answers on a web map! PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/LTDBTU/ Lake Thoreau Nathan McEachen PUBLISH N3G8Q8@@talks.staging.osgeo.org -N3G8Q8 Exploring Multimodal LLMs for Remote Sensing with LibreGeoLens en en 20251103T130000 20251103T160000 3.00000 Exploring Multimodal LLMs for Remote Sensing with LibreGeoLens In this hands-on workshop, we introduce Multimodal Large Language Models (MLLMs) and explore their potential for analyzing remote sensing imagery. Participants will get a live demo of [LibreGeoLens](https://plugins.qgis.org/plugins/libre_geo_lens/), an open-source QGIS plugin that enables testing and comparing AI models within a familiar GIS environment. LibreGeoLens is designed for rapid experimentation and prototyping, not to replace workflows, but to help users evaluate emerging models and identify opportunities for integration into real-world geospatial analysis pipelines. After the demo, participants will install and configure QGIS and LibreGeoLens, then follow a guided, step-by-step tutorial to explore the plugin's capabilities. In the final portion of the workshop, participants are encouraged to test models on their own imagery or use cases, share insights with others, and contribute feedback to inform future development. All skill levels welcome. No programming experience required. PUBLIC CONFIRMED Pre-Conference Workshop https://talks.staging.osgeo.org/foss4g-na-2025/talk/N3G8Q8/ Lake Thoreau Pedro Uria PUBLISH FLUEPT@@talks.staging.osgeo.org -FLUEPT GDAL's new command line interface - and other updates en en 20251104T103000 20251104T110000 0.03000 GDAL's new command line interface - and other updates Sponsorship of the GDAL project has enabled a number of exciting developments, including a highly-requested replacement for the 25-year old command line interface. This talk will provide a demonstration of the GDAL's new CLI and other features from recent releases, such as new capabilities for the VRT virtual raster format. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/FLUEPT/ Regency Ballroom B Dan Baston PUBLISH FUYA37@@talks.staging.osgeo.org -FUYA37 DuckDB + Rasters: Hexagons For Blazing Fast Analytics en en 20251104T110000 20251104T113000 0.03000 DuckDB + Rasters: Hexagons For Blazing Fast Analytics DuckDB is rapidly becoming the FOSS tool of choice for fast, local analytics. While its extension ecosystem has added geospatial support, there isn't a clear way to perform raster analytics inside DuckDB. In this talk, we will discuss our experience at Fused using DuckDB and open source extensions like h3-duckdb to perform fast raster analytics. Raster datasets present unique challenges for analytics platforms. While vector-friendly databases can easily represent points, lines, and polygons, the gridded nature of rasters doesn't translate naturally to traditional database structures. While some have tried converting rasters to vector geometries, these approaches lack the performance that makes DuckDB attractive. Our approach utilizes a different option, specifically employing the H3 hexagonal hierarchical spatial index to reaggregate raster space into manageable analytical units. Hexagonal grids offer advantages over traditional square pixels for analytical purposes, including more uniform adjacency relationships (all neighbors are equidistant), and better approximation of circles, minimizing sampling bias. The H3 library, originally developed by Uber and now an open-source project, provides an ideal framework for this approach with its global hierarchical hexagonal grid system. In this talk, we describe the preprocessing of raster data, integration of analytic tooling with DuckDB, the SQL analytics using DuckDB, and how we returned and visualized results. We'll demonstrate case studies where this approach has proven effective, including: * USDA Cropland Data Layer (CDL) * Weather data (ERA5) * Digital elevation models (DEM) While powerful, our approach does have limitations. Operations requiring pixel-perfect precision or very high-resolution outputs may still require traditional raster tools. Additionally, initial conversion of large raster datasets to the H3 format introduces overhead. The combination of DuckDB's analytical power with H3's spatial indexing provides a remarkably effective approach to raster analytics. This talk will provide attendees with practical knowledge about implementing similar systems, code examples for common analytical tasks, and insights into performance optimization. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/FUYA37/ Regency Ballroom B Isaac Brodsky Sina Kashuk PUBLISH YTCPRS@@talks.staging.osgeo.org -YTCPRS Is Zarr the new COG? en en 20251104T113000 20251104T120000 0.03000 Is Zarr the new COG? Cloud-Optimized GeoTIFF (COG) and Zarr have each earned their place in modern geospatial workflows. While often framed in opposition—raster vs. analysis, imagery vs. data cube—they are in fact deeply complementary. In this talk, we’ll unpack how they address similar challenges from different angles, and why they should be considered parts of a shared toolkit rather than competing paradigms. We’ll highlight where Zarr and COG overlap, where they differ, and how decisions around chunking, compression, tiling strategies, and metadata design affect both formats. We'll discuss implementation pitfalls, emerging best practices, and the still-unanswered questions that data producers and tool builders face. More than a comparison, this talk is a call to action: the community lacks clear guidance and consistent support for practitioners working to produce data in either format. We’ll highlight concrete gaps in the tooling landscape, share ideas from our own work on how to improve decision-making and best practices, and invite others to collaborate on building a healthier, more cooperative open geospatial data ecosystem. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YTCPRS/ Regency Ballroom B Jarrett Keifer Julia Signell PUBLISH ZAWNNF@@talks.staging.osgeo.org -ZAWNNF STAC Beyond Rasters en en 20251104T130000 20251104T133000 0.03000 STAC Beyond Rasters STAC (SpatioTemporal Asset Catalog) lets people discover and filter datasets to find exactly which data they need to answer their questions. It is a flexible specification and that flexibility can be both powerful and a bit of a curse. It can be hard to figure out how to do things “right” – in a way that will integrate well with other catalogs and be well supported by tooling. STAC has seen significant adoption in the earth observation community across many different scales of data producers including large government efforts like [Landsat](https://landsatlook.usgs.gov/stac-server/) and large commercial efforts like [Earth Search](https://earth-search.aws.element84.com/v1/) and [Microsoft’s Planetary Computer]( https://planetarycomputer.microsoft.com/api/stac/v1). Many of these STAC implementations contain exclusively satellite imagery. They have a collection for each data product (for instance “landsat-c2-l2”) and within that collection there is an item for each scene. The items contain assets pointing to COGs on object storage. That is a well-documented way to use STAC. But STAC is not just for satellite imagery. STAC is a specification for defining and searching any type of data that has spatial and temporal dimensions. For example you can use standalone collections and the Datacube Extension to represent n-dimensional data cubes (such as an earth system model stored in Zarr). Or you can catalog all the sensor data captured during a research cruise using one collection per data type. To capture that kind of data you need to know how much metadata to capture at the STAC level and how to structure your STAC hierarchy. This talk will go through the interconnected decisions and discuss how to weigh priorities to maximize the usefulness of your STAC. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/ZAWNNF/ Regency Ballroom B Julia Signell PUBLISH KTVHWS@@talks.staging.osgeo.org -KTVHWS VirtualiZarr: cloud-optimized access to archival-format datacubes without duplication en en 20251104T133000 20251104T140000 0.03000 VirtualiZarr: cloud-optimized access to archival-format datacubes without duplication Zarr has emerged as a flexible data format for storing cloud-optimized self-describing n-dimensional data cubes. It’s great! There is just one problem. The vast majority of data that is being generated and distributed right now is not in Zarr stores — it’s in NetCDF, or HDF5, or COG. Those legacy data formats all support partial reads, but they aren’t optimized for cloud access (except for COG). What this means is that they require many small network requests to fetch bits of metadata that are sprinkled throughout the file and — when concatenating — across many different files. One solution is to copy data over to Zarr. But this means you double your storage cost and introduce a reprocessing step. That’s where virtual Zarr stores come in. Virtual Zarr stores capture metadata about where particular chunks of data are stored and allows tools like xarray to access the data from the chunks directly using HTTP range requests. You never need to duplicate the data to Zarr. These virtual Zarr stores can be written to disk using kerchunk or icechunk and distributed alongside data. So how does VirtualiZarr fit in? VirtualiZarr is the easiest way to construct virtual Zarr stores out of thousands of individual data files. It takes advantage of the powerful merging and concatenation logic that xarray already has and lets you apply those same methods to virtual Zarr stores. VirtualiZarr provides readers for many legacy data formats so you can read the metadata from the original files and produce a virtual Zarr store with the combined metadata and access paths. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/KTVHWS/ Regency Ballroom B Julia Signell Chuck Daniels PUBLISH G3MHUZ@@talks.staging.osgeo.org -G3MHUZ State of STAPI: A community tasking standard en en 20251104T140000 20251104T143000 0.03000 State of STAPI: A community tasking standard Community standards, created through collaborative grassroots efforts before being widely adopted, play a crucial role in geospatial interoperability as exemplified by specifications like SpatioTemporal Asset Catalog (STAC) and Cloud-Optimized GeoTIFFs (COGs). Efforts like these not only enable seamless data interoperability but also form the backbone of robust, scalable systems that support critical geospatial operations. The Sensor Tasking API (STAPI) is an emerging community standard aiming to standardize sensor tasking and spatiotemporal data ordering through a unified API and an ecosystem of tooling. Five community sprints have been held across the US and Europe, most recently this past April in Lisbon, where the spec reached the major milestone of a version 0.1.0 release. Featuring collaboration amongst government groups, commercial satellite operators, data integrators, and other community members, these iterative and collaborative sprints have worked and continue working toward developing a robust API specification and tooling. A sixth sprint is in the works and expected to take place in Europe in the first half of 2026. This talk will showcase the concrete achievements of the STAPI community, demonstrating how a collaborative approach can lead to a tangible and impactful standard for accessing future geospatial data. We will delve into the specification, highlighting its key features and recent developments, including the upcoming version 0.2.0 release. We will also look at the open-source ecosystem growing out of this effort, including projects like stapi-fastapi, stapi-pydantic, and pystapi-client that are empowering the community to create their own STAPI-compliant services and tooling, and several practical implementations from commercial providers. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/G3MHUZ/ Regency Ballroom B Matthew Hanson Jarrett Keifer PUBLISH BLMK9V@@talks.staging.osgeo.org -BLMK9V STAC Best Practices en en 20251104T143000 20251104T150000 0.03000 STAC Best Practices As the adoption of the SpatioTemporal Asset Catalog (STAC) continues to grow, understanding its core principles is essential for both users and data providers looking to leverage Earth observation data. This presentation will cover the foundational aspects of STAC, explaining how it enables better discovery, sharing, and analysis of geospatial assets through a unified metadata framework. Attendees will learn about the core STAC specification, its key concepts, and the importance of its open-source ecosystem. We will highlight the role of STAC in overcoming data fragmentation in geospatial workflows, and how its extensibility supports a wide range of applications. Additionally, we’ll address lessons learned from early implementations and offer practical tips on how to use STAC effectively. For data providers, this session will cover best practices for STAC adoption, including guidance on selecting the right extensions for different use cases. Users will also gain insight into how to query and explore STAC data efficiently. Whether you’re just starting with STAC or looking to deepen your understanding, this session will equip you with the knowledge needed to navigate and contribute to the growing STAC ecosystem. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/BLMK9V/ Regency Ballroom B Matthew Hanson PUBLISH N9XQ8K@@talks.staging.osgeo.org -N9XQ8K Mapping Community Capital: Revealing Local Gaps in Rural Access en en 20251104T153000 20251104T160000 0.03000 Mapping Community Capital: Revealing Local Gaps in Rural Access The Community Capitals Framework (CCF) offers a structured approach to assessing community strengths and weaknesses across seven forms of capital—natural, cultural, human, social, political, financial, and built. Over the past several years, our team has collected and analyzed data indicators aligned with this framework to evaluate the strengths and weaknesses of cities and counties in the Midwest United States. Drawing on publicly available data, we’ve assessed how various forms of capital, such as built infrastructure, human services, and social networks vary across communities. While these assessments have offered valuable insights at the county and city level, they often mask important disparities within communities themselves. Our current focus shifts to analyzing those same indicators at a finer spatial scale to better understand how access to critical services and assets is distributed within individual communities. To uncover these internal disparities, we apply spatial analysis techniques using free and open-source software (FOSS) and openly available geospatial data. We begin by analyzing access to key community assets such as parks, healthcare facilities, broadband infrastructure, and educational institutions across a multi-county rural region, identifying differences in regional availability. We then zoom in on selected case study communities to map the neighborhood-level distribution of those same assets, revealing gaps in access that are often hidden by broader county- or city-level aggregates. Using free and open-source tools we perform proximity analysis, service area analysis, and spatial overlays to identify underserved areas—places where residents may face barriers to opportunity, infrastructure, or essential services. Our case studies illustrate how a place-based, spatial lens grounded in open data tools can guide more equitable rural planning, investment, and community engagement. This session will highlight our methods, case study findings, and how these insights can inform targeted investment in small rural communities. Our reproducible, open-source workflow is designed for use by researchers, planners, and community stakeholders aiming to apply geospatial analysis in low-resource environments. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/N9XQ8K/ Regency Ballroom B Christopher J. Seeger Bailey Hanson PUBLISH XMNUK9@@talks.staging.osgeo.org -XMNUK9 Supporting FOSS4G on Boston University's High Performance Cluster en en 20251104T160000 20251104T163000 0.03000 Supporting FOSS4G on Boston University's High Performance Cluster The goal of this talk is to provide insight on how Boston University (BU) supports GIS workflows that use GIS open source software on our High Performance Computing Cluster and provide examples of what kinds of research is being done using these workflows. I am a scientific programmer/analyst at BU and I am part of Research Computing Services. My group maintains and supports our high performance cluster (HPC). I am part of the Applications Team and my group provides onboarding services and training for researchers, students, and staff at BU, and my specific area of expertise is in GIS computing and workflow development. BU's HPC resources is available to use by any community member of our 17 colleges and schools. Our cluster runs an Alma8 Linux operating system and uses a customized Sun Grid Engine as the scheduler. We have approximately 7,000 processes, 236 GPUS, two petabytes of research data, and over 2,500 users. The cluster has over 1,300 software packages installed to support research and approximately 50 software packages are associated with GIS workflows, with majority of them being open source software, libraries, or standards. This includes GDAL, GrassGIS, QGIS, PostGIS, NetCDF, Zarr, Orfeo Toolbox, Climate Data Operators (CDO), GEOS, PROJ, PDAL, and R and Python GIS libraries. Our HPC system supports running jobs as batch jobs, but also interactive jobs, so researchers can map their GIS data using programs like QGIS, or plotting libraries from R and Python. As part of my role, I provide training and consulting on how to use these open source software on our cluster. I also provide support on how to best optimize their GIS workflow to take advantage of parallelization and also how to process very large datasets. With supporting 17 colleges and schools, I see the different ways opens source geospatial software is used at the university. Some example research projects include: - Center for Remote Sensing running phenology change analysis of the entire continental US for span of a decade. - Public health projects that model which communities are impacted by PM2.5 air pollutants. - Biology department tracking monarch butterfly migrations. - Dental School project identifying service area gaps coverage of dental offices in the state of Massachusetts. For my talk I will cover the following three items: 1.) Provide a basic understanding of what is a high performance cluster and how do our researchers interact with it. 2.) An overview of what types of open source software for geospatial is used on our cluster. 3.) Provide examples of research that is done using open source software for geospatial on our cluster. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XMNUK9/ Regency Ballroom B Dennis Milechin PUBLISH SJU3MN@@talks.staging.osgeo.org -SJU3MN Academic Birds of a Feather en en 20251104T163000 20251104T170000 0.03000 Academic Birds of a Feather Come network with current, future, and former academics! Whether you're currently in academia or would like to be, come meet other academics who use open source geospatial software for both teaching and research. PUBLIC CONFIRMED Birds of a Feather https://talks.staging.osgeo.org/foss4g-na-2025/talk/SJU3MN/ Regency Ballroom B Michele Tobias PUBLISH VPUPNR@@talks.staging.osgeo.org -VPUPNR Vector tiles and GeoServer: dynamic tiles server and base maps en en 20251104T103000 20251104T110000 0.03000 Vector tiles and GeoServer: dynamic tiles server and base maps Mapbox vector tiles have emerged as a popular format for delivering geospatial data, offering dynamic rendering and interactivity for modern web maps. While not an official OGC standard, this open specification has been widely adopted, making it a staple of web cartography. This presentation delves into GeoServer's evolving capabilities to serve Mapbox vector tiles, emphasizing recent enhancements and best practices. We will explore how GeoServer leverages SLD and CSS to define the contents of vector tiles, ensuring tailored and efficient data delivery. New configuration options, such as label point generation, attribute selection and geometry coalescing, will be highlighted as tools to control and optimize tile outputs. Practical advice will also be provided for streamlining vector tile generation, helping users create seamless and scalable workflows. The session will conclude with a look at how vector tiles can serve as an input for generating high-quality base maps in various coordinate reference systems. Using OpenMapTiles styles and Planetiler, we will demonstrate how to produce visually appealing, multi-projection base maps, unlocking the full potential of vector tiles for diverse applications. Whether you're building interactive maps or generating custom base maps, this talk will equip you with the knowledge and tools to make the most of GeoServer's vector tile capabilities. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VPUPNR/ Reston ABC Simone Giannecchini PUBLISH 7HWLUK@@talks.staging.osgeo.org -7HWLUK OGC APIs with GeoServer: implementation, availability, and next steps en en 20251104T110000 20251104T113000 0.03000 OGC APIs with GeoServer: implementation, availability, and next steps The OGC APIs are a fresh take at doing geo-spatial APIs, based on WEB API concepts and modern formats, including: * Small core with basic functionality, extra functionality provided by extensions * OpenAPI/RESTful based * JSON first, while still allowing to provide data in other formats * No mandate to publish schemas for data * Improved support for data tiles (e.g., vector tiles) * Specialized APIs in addition to general ones (e.g., DAPA vs OGC API - Processes) * Full blown services, building blocks, and ease of extensibility This presentation will provide an introduction to various OGC APIs and extensions, such as Features, Styles, Maps and Tiles, STAC and CQL2 filtering. Some of specs are finalized and complete enough that they have a GeoServer supported extensions, while others are provided as community modules. Join us to find out the current state of implementation, our future steps, and how you can participate in it. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/7HWLUK/ Reston ABC Andrea Aime Simone Giannecchini PUBLISH VUMVGS@@talks.staging.osgeo.org -VUMVGS What’s new in CesiumJS and the 3D Geospatial Ecosystem en en 20251104T113000 20251104T120000 0.03000 What’s new in CesiumJS and the 3D Geospatial Ecosystem Cesium is an open-source 3D geospatial visualization platform for building web-based, high-performance virtual globes and maps. This talk will highlight recent capabilities for viewing and analyzing geospatial data in the Cesium runtimes, with a primary focus on CesiumJS. The first section of the talk will highlight new features broadly available across the Cesium ecosystem and how to use them in CesiumJS. CesiumJS now supports showing Gaussian splats, a 3D rendering technique that represents points as smooth Gaussian functions, enabling fast, photorealistic visualization of real-world scenes. CesiumJS and game engine runtimes also announced broad support for voxel rendering of volumetric data, which has broad applications to rendering data formatted as a grid of small cubes (voxels), each representing a value in space, such as density or intensity. CesiumJS and game engine runtimes also introduced support for time dynamic data via support for time dynamic 3D Tiles, which provides to capability to visualize patterns, values, or spatial relationships change over time, enabling clearer insights into trends, movement, and temporal behavior. Finally, Cesium offers increased support for viewing architectural design models representing things like buildings and physical infrastructure. The capability is enabled via 3D Tiles and includes broad support to preserve geometric detail and metadata regarding design details and materials. CesiumJS and all game engine runtimes heavily rely on and fully support rendering of 3D Tiles 1.1, an open standard for streaming and rendering massive, heterogeneous 3D geospatial datasets over the web. 3D Tiles were originally developed by Cesium before becoming an Open Geospatial Consortium (OGC) community standard to encourage broad interoperability. 3D Tiles feature prominently in many new capabilities in the Cesium ecosystem highlighted in the first section of this proposed talk, but the underlaying details of 3D Tile technology are proposed to be covered more in depth in a separate 2025 FOSS4G presentation while this talk will focused on visualization and analysis of geospatial data with CesiumJS and other runtimes. The next section of the talk will highlight new features specific to the CesiumJS JavaScript library. CesiumJS now allows draping imagery on 3D Tiles, enabling greater flexibility in viewing raster layers on top of 3D data in the same scene. CesiumJS also completed a series of visual quality improvements, including improvements to ambient occlusion (enhancing depth and spatial perception), environment maps to support image-based lighting (for more accurate real world lighting, reflections, and ambient light behavior), physically-based material (PBR) improvements (for metals and other specular reflections) and tone mapping to achieve better color consistency across rendering engines. Finally, the team is working towards better support for OGC vector feature data, with the target benchmark to performantly support datasets with 100 Million features in 3D scenes. These capabilities will enable better presentation of representing real-world geographic vector features—such as roads, buildings, or rivers—along with their associated attributes and geometry alongside all other 3D assets available in a Cesium scene. The next section of the talk will highlight improvements aimed at developer experience. First, the team revamped the Cesium Sandcastle Tool, an interactive application and coding environment that lets developers experiment with the library in real time to accelerate learning and prototyping without the need for a full project setup. The revamp greatly improves the code editor portion of the tool and other elements to improve ease of use. Additionally, Cesium is now on the path to providing greater UI and UI framework support for working with CesumJS. The iTwin.js library, an open-source JavaScript library developed by Bentley Systems for building web applications that visualize, analyze, and interact with infrastructure digital twins, will provide React UI components supporting general user interface patterns based around the Cesium viewer. The final section of the talk will highlight several integrations with foundational geospatial software tools and increased integration and availability of useful geopsaptial datasets. For geospatial tool integrations, we will highlight additions to the Cesium QGIS plugin which allows streaming of 3D tiles – either from Cesium ion or other services that stream tiles – into a QGIS project. This section will also highlight integrations to make 3D Tiles available in Three.js, the most popular library for rendering general purpose 3D graphics on the web, and WebODM, the open-source, web-based platform for processing aerial imagery into geospatial products such as orthophotos, 3D models, point clouds, and digital elevation models (DEMs) using photogrammetry. Finally, building on Cesium’s core focus on making massive scale 3D data broadly accessible for visualization, Cesium continues to curate and make available global datasets. Cesium has planned updates to Cesium World Terrain this year, and continues to make available other massive scale datasets like Cesium OSM Buildings, Cesium World Bathymetry and also recently released Cesium Moon Terrain in 2024. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VUMVGS/ Reston ABC Luke McKinstry PUBLISH TRMHTE@@talks.staging.osgeo.org -TRMHTE Astral: A spatial extension for the decentralized web en en 20251104T130000 20251104T133000 0.03000 Astral: A spatial extension for the decentralized web Astral is a spatial extension for decentralized systems like Ethereum (i.e., distributed ledgers) and the InterPlanetary File System (IPFS) (i.e., peer-to-peer networks). Built on FOSS principles and designed for composability, Astral introduces a standard way to structure, sign, and work with geospatial data in trust-minimized environments — enabling location to become a first-class primitive in decentralized applications and protocols that often lack standardized support or completely overlook the geospatial dimension. This talk will introduce the decentralized geospatial web, along with Astral’s architecture, including: - **The Location Protocol** — a general-purpose, open standard for digitally signed spatial records or claims. These “location attestations” are versatile, and can represent the location of events, objects, features, and interactions. The Location Protocol supports an extensible range of geospatial feature types, data formats, and location proofs, including formal cryptographic evidence and informal supporting media, as well as arbitrary metadata. - **The Location Proof Extensions Library** — a modular collection of “recipes” for generating location-linked evidence using hardware, peer confirmation, sensors, cryptographic techniques and other proof-of-location strategies to support claims about location. - **Spatial.sol** — a suite of utilities for reasoning about location natively in smart contracts, including basic spatial operators (e.g., contains, distance). - **Supporting tools** like the Astral SDK and API, which simplify the creation, verification, and querying of location attestations. We’ll walk through how Astral enables new workflows for verifying geospatial data and computation, and how it integrates with existing standards and practices from the FOSS4G world. ## Why Now The Web3 paradigm, which includes consensus networks, blockchains, smart contracts, content-addressed data, decentralized identifiers and more, introduces new architectural constraints and opportunities for spatial data. Well-designed Web3 systems are more open and durable, and enhance user rights, by incorporating features such as immutability, attribution, tamper-evidence, and selective disclosure. But current geospatial systems were never designed to interoperate with distributed ledgers or peer-to-peer networks, and these decentralized systems do not typically offer structured and standardized ways to work with geospatial data. Astral fills this gap. It provides a minimal, standards-aligned foundation for building spatially-aware applications on decentralized systems, without requiring a significant break from existing geospatial tools. It’s built for compatibility with formats like GeoJSON and concepts from spatial data infrastructure (SDI), and designed to be extended by communities who already know how spatial data should work. This is slated to become increasingly important as the nascent AI agent economy develops and becomes one of the largest creators and consumers of geospatial data. ## What We’ll Cover - The structure and rationale of the Location Protocol - How different location proof strategies are modularized in the Location Proof Extensions Library - Spatial.sol and on-chain verifiable geocomputation - Current deployments on Celo, Arbitrum, Base, and Sepolia - Example applications: AI agent localization and geospatial data interchange, verifiable check-ins, field data collection, decentralized mapping - Opportunities for collaboration on tooling, verification schemes, and data interoperability ## Track **Data Management and Interoperability** — Astral is explicitly designed to bridge decentralized and traditional spatial systems through shared schemas, open formats, and cross-platform tooling. ## Who This Is For - Developers exploring decentralized approaches to geospatial data - GIS professionals interested in cryptographic verification, decentralized IDs, or tamper-proof provenance - Contributors to open geospatial standards who want to shape how those standards evolve in new computing environments ## Open Source Commitment Astral is fully open source. All schemas, contracts, recipes, and code are maintained in public repositories as part of the Decentralized Geospatial Collaborative through the University of Maryland, and contributions from the FOSS4G community are welcome — especially around proof strategies, media types, and integration with open geospatial tools. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/TRMHTE/ Reston ABC Taylor M. Oshan John R Hoopes PUBLISH SSRV9E@@talks.staging.osgeo.org -SSRV9E Handling 3D Data in QGIS en en 20251104T133000 20251104T140000 0.03000 Handling 3D Data in QGIS In the talk, we would like to give a comprehensive overview of what can be achieved with 3D geospatial data in QGIS. The main focus will be on the visualization of geospatial data in 3D scenes, but we would like to cover many topics such as: - what data types / formats may be used in 3D scenes - customization of 3D rendering - measurement tools, elevation profiles and cross sections - animations and 3D scenes in print layouts - globe 3D scenes - vertical coordinate reference systems - processing algorithms for 3D data - editing of 3D data in QGIS - using 3D Tiles - our recent additions to QGIS 3D project - brief history of the QGIS 3D project and how development of QGIS 3D gets funded (crowdfunding campaigns, grants, GSoC, ...) - future plans for QGIS 3D PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/SSRV9E/ Reston ABC Martin Dobias PUBLISH QTM8PB@@talks.staging.osgeo.org -QTM8PB Mergin Maps and QGIS for Field Surveys en en 20251104T140000 20251104T143000 0.03000 Mergin Maps and QGIS for Field Surveys Join the core development team of Mergin Maps from Lutra Consulting (Europe) to explore the latest advancements in this powerful open-source platform. Designed to streamline field data collection, Mergin Maps is powered by QGIS and recommended by the QGIS.org project as one of the solution for field data capture within its ecosystem. This session will first outline the comprehensive Mergin Maps ecosystem. We’ll explore its components: the QGIS Plugin, the Mergin Maps server, and our intuitive mobile application (available for Windows, iOS, and Android). We will also touch upon its integration capabilities, including with databases like PostgreSQL. Subsequently, we will highlight significant developments from the past year. Expect insights into new features such as map annotations for richer field notes, Single Sign-On (SSO) for enhanced security and user management, and improved photo capturing functionalities, among other innovations designed to boost your productivity. At the end, we will show real-world use cases, demonstrating Mergin Maps effectively operating in demanding production environments. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/QTM8PB/ Reston ABC Peter Petrik PUBLISH YSFUFY@@talks.staging.osgeo.org -YSFUFY QGIS Processing Nodes Project Update en en 20251104T143000 20251104T150000 0.03000 QGIS Processing Nodes Project Update The QGIS Processing Nodes (QPN) project is a multi-phase project that will attempt to bring node-based processing (ex. Blender's shader and geometry nodes) to QGIS. I gave an initial talk about the concept at FOSS4G NA 2023 in Baltimore during a Lightning Talk session. This presentation will be going over the purpose and goals, talk about the conceptual road map, and present the current proof-of-concept model editor for the project. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YSFUFY/ Reston ABC Ethan Snyder PUBLISH PPKHT8@@talks.staging.osgeo.org -PPKHT8 Piggybacking on QGIS Processing Framework to Build Scientific Software Tools en en 20251104T153000 20251104T160000 0.03000 Piggybacking on QGIS Processing Framework to Build Scientific Software Tools The QGIS Processing Framework is the bedrock of geospatial algorithms that we use in QGIS, such as Clip, Buffer, Merge, etc., but it is not just that. It also empowers developers and scientists to create robust scientific software suites in the form of QGIS plugins that are integrated with open-source geospatial ecosystems. The talk will cover: 1. All the good things that come with the QGIS Processing Framework: its versatility, command-line execution, containerization, seamless GDAL/QGIS integration, and file I/O. 2. How to leverage it to turn your GIS workflows into QGIS plugins—from prototyping using the Graphical Modeler to turning graphic models into scripts (Python) and full plugins. The talk will then exemplify this with two case studies: Curve Number Generator: A QGIS plugin that automates the generation of Curve Number datasets for any area of interest. We will trace its evolution from a manual workflow to a community software. QNSPECT: NOAA’s QGIS plugin for estimating nonpoint pollution/erosion over a watershed. We will dissect the plugin’s architecture—how it taps into GDAL and GRASS under the hood and delivers rapid, scenario-based analyses within QGIS. Attendees will learn tips for dynamically constructing mock GUIs for their GIS workflows, isolating geoprocessing steps for testing, and packaging plugins with the ‘Plugin Builder’ plugin. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PPKHT8/ Reston ABC Abdul Raheem Siddiqui PUBLISH RZMU79@@talks.staging.osgeo.org -RZMU79 QGIS Birds of a Feather en en 20251104T160000 20251104T163000 0.03000 QGIS Birds of a Feather Meet for 30 minutes and talk about QGIS US and where that stands. WE also discuss the possible formation of a board because by that point I hope we have some sort of non-profit status. PUBLIC CONFIRMED Birds of a Feather https://talks.staging.osgeo.org/foss4g-na-2025/talk/RZMU79/ Reston ABC Randal Hale PUBLISH PDFAMJ@@talks.staging.osgeo.org -PDFAMJ Refining Culvert Detection in Elevation Derived Hydrography with Deep Learning en en 20251104T163000 20251104T170000 0.03000 Refining Culvert Detection in Elevation Derived Hydrography with Deep Learning Accurate water flow modeling from DEM requires accounting for man-made features like culverts and associated influential structures, which are often unrepresented within digital elevation models (DEMs). Additional hydro-enforcement treatments applied to DEMs are crucial for accurate modeling and informed decision-making in the water infrastructure management domain. The challenge, however, of detecting and accurately incorporating into the model, features like culverts, can be particularly daunting when dealing with large geographic areas and complex topography. This presentation provides an approach with an updated methodology for culvert detection that addresses this challenge as it is applied to LiDAR-based elevation-derived hydrography (EDH). Building on early methodological developments and drawing on existing data from almost 10 years ago, the approach is refined with some novel open-source data processing elements. The success of these image detection methods for culverts is reliant on multidirectional Sky View Factor (SVF) imagery created from the DEM as analyzed through the Relief Visualization Toolbox (RVT) Python library. The SVF images are subdivided and tessellated into small image subsets to maintain maximum feature fidelity or resolution during a subsequent custom trained deep-learning approach. This tiling allows the trained model to rapidly predict small features such as culverts over vast geographic areas while maintaining the input high-resolution detail from the initial DEM input SVF data. The versatility and potential of SVF imagery as a predictor of such infrastructure is also demonstrated in detection of related man-made features, including berms and cattleguards. The transferability of the model trained with culvert data from a 3000 square mile Santa Fe County, New Mexico 3DEP LiDAR dataset is also demonstrated through application over a geographically distinct large 40 billion raster cell elevation dataset. Details are presented to show the performance and computational demands of these predictions on a variety of computing platforms and operating systems. While this culvert feature prediction technique certainly highlights some achievements in the detection, it also uncovers some potential drawbacks including overprediction which require additional geospatial data management techniques. Employed techniques include spatial proximity filtering and data-driven culvert endpoint refinements for determining a “best-use” culvert cutline suitable for ultimate DEM enforcement in EDH applications. Notwithstanding potential geospatial data management challenges, the approach's ability to rapidly detect culverts, independent of any potential drawbacks, serves as a powerful accelerant to the subsequent flow modeling stage, enabling timely access to the necessary ingredients of hydro-enforcement. Accurate 3D culvert feature geometry cutlines then inform DEM hydro-enforcement. Next, iterative flow modeling workflow procedures in GRASS hydrological toolsets can be deployed. Here again, the computational demands of hydrological processes can be challenging for large area data processing, especially as related to computational memory requirements. The scalability limits of these core GRASS processes were tested with results presented comparing computational benchmarks between 10 billion raster cell elevation versus 40 billion raster cell elevation datasets on various hardware platforms. The presented culvert detection workflow in LiDAR-based elevation-derived hydrography has significant implications for water infrastructure management. As high-resolution datasets and advanced sensor technologies continue to expand, it will be imperative to deliver faster, more efficient, and effective means to process and extract information from data. The highlighted approach facilitates more rapid detection of elements that impact flow modeling through elevation-derived hydrography processing. Combining cutting-edge technology with growing societal needs for informed data models, these infrastructure detection and modeling approaches do enhance the ability of geospatial professionals to deliver timely and accurate information for resiliency planning in support of the water infrastructure management community. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PDFAMJ/ Reston ABC Robert Dzur Federico Kurten PUBLISH L9U8VC@@talks.staging.osgeo.org -L9U8VC Cloud-Native Geospatial in QGIS en en 20251104T103000 20251104T110000 0.03000 Cloud-Native Geospatial in QGIS Cloud-native geospatial has gained a lot of traction, and while many people see their benefits mainly in the context of processing large amounts of data in the cloud, the formats and protocols can be successfully used also in the more traditional desktop GIS tools like QGIS. At Lutra Consulting, we have been interested in this topic and we have implemented support for COPCs in QGIS 3.26 (back in 2022) and more recently, support for STAC in QGIS 3.42 (released early 2025). The talk is not going to dive into technical details of various formats, but rather it will try to show what is currently possible to achieve with STAC catalogs for data discovery, and with raster, vector and point cloud layers when being streamed. We will look into the limitations of the current support and scope for future improvements. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/L9U8VC/ Lake Audubon Martin Dobias PUBLISH EWD7AJ@@talks.staging.osgeo.org -EWD7AJ Cloud-Native Humanitarian Maps with eoAPI en en 20251104T110000 20251104T113000 0.03000 Cloud-Native Humanitarian Maps with eoAPI OpenAerialMap (OAM), a Humanitarian OpenStreetMap Team (HOT) project, is an open data platform providing access to high-resolution satellite and drone imagery. It was initially developed in 2015 and in the decade since it has served the community well, but systems had become increasingly difficult to maintain. In the absence of more widely adopted community standards, they had initially developed their own metadata catalog and were therefore responsible for maintaining it. Older technologies often stop receiving support after a certain time, as well. HOT is a lean team that works among the open source community, so maintainability is key. Moving OAM’s metadata catalog to the SpatioTemporal Asset Catalog (STAC) specification made arguably the biggest impact in maintainability by allowing HOT to lean on community-supported solutions. The spec alone would cover the metadata schema and inform some of the storage design, but wide adoption amongst satellite imagery providers and major space agencies has helped drive further development. eoAPI takes it one step further by tying a selection of STAC-compatible services together into a feature-rich earth observation backend. The core eoAPI deployment stands up metadata, raster, and vector services, all backed by a PostgreSQL database optimized for STAC. This serves as a trusted foundation for further development specific to OAM’s needs since HOT no longer has to act as sole maintainer for any of the discrete services or their interconnections. We’ll discuss eoAPI through the lens of OAM as an integrated, open-source solution, comprising of: - Custom STAC extension for migrating legacy geospatial data to STAC - pgSTAC + STAC-FastAPI for spatiotemporal search - TiTiler for on-demand tile generation directly from source imagery And we’ll discuss other technical highlights of working with HOT and OAM: - Integrated external catalogues in addition to OAM - Built a prototype frontend using MapLibre that connected directly to eoAPI - Backend services are deployed via Helm, streamlining deployment and configuration - Deployment monitoring via custom metrics and dashboards We’ll share live products, code examples, and the architectural decisions that helped us build this new version of OpenAerialMap. Attendees will learn why eoAPI is a good fit for geospatial work flows and even some practical strategies for modernizing their geospatial infrastructure using eoAPI. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/EWD7AJ/ Lake Audubon Ali Ziel Indraneel Purohit PUBLISH ELXJMD@@talks.staging.osgeo.org -ELXJMD Large-Scale Geospatial Analysis with Open-Source h3-indexer en en 20251104T113000 20251104T120000 0.03000 Large-Scale Geospatial Analysis with Open-Source h3-indexer With the rise of new cloud-native geospatial tools, an increase in data formats and even more ways to store your data, the h3-indexer addresses the growing challenge of efficiently converting diverse spatial data formats into a unified, analysis-ready representation at scale. Originally developed for Amazon's Air Quality initiatives to calculate emissions globally at highly granular levels, this open-source tool fills a significant gap in the cloud-native geospatial technology landscape. The geospatial community faces a persistent challenge in standardizing spatial data processing workflows, particularly in cloud environments. Traditional GIS tools often lack the scalability needed for modern big data applications, while cloud-native solutions frequently require custom compute environments and complex builds that are too complicated for most use cases. Organizations working with diverse geospatial datasets spanning applications from environmental monitoring to urban planning, struggle to harmonize data from multiple sources into a consistent, comparable structure in order to calculate key metrics. The h3-indexer leverages Uber's H3 global grid system to transform any number of point, line, and polygon geometries into standardized hexagonal grids. Built on PySpark and Apache Sedona, the tool is designed for cloud-native deployment and can process massive geospatial datasets in distributed computing environments. Through JSON-based configuration, users can version, share, and reproduce spatial processing workflows across different environments and datasets. The H3-Indexer's modular architecture makes it highly extensible for community contributions and customization. The tool is built around a flexible data model system using pydantic that defines clear interfaces for different input types (VectorTable, RasterFile), geometry types (POINT, LINE, POLYGON) and processing methods (WITHIN, PCT_LENGTH, PCT_AREA). This design allows developers to easily add new geometry processing methods by simply extending the existing enums and implementing corresponding router functions, or introduce entirely new data source types by creating new base model classes that follow the established patterns. The configuration-driven approach means that new attribution methods, data connectors, or processing algorithms can be integrated without modifying the core indexing and resolution logic. This modularity extends to data sources as well. Current support for S3, Glue Catalog, and various file formats demonstrates how new input connectors can be integrated into the existing validation and processing framework, making the tool adaptable to diverse organizational needs and emerging geospatial data standards. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/ELXJMD/ Lake Audubon Madie PUBLISH 9E77XF@@talks.staging.osgeo.org -9E77XF Cloud-Native Geospatial Metadata with stac-geoparquet en en 20251104T130000 20251104T133000 0.03000 Cloud-Native Geospatial Metadata with stac-geoparquet stac-geoparquet is an open, community data storage specification for the SpatioTemporal Asset Catalog (STAC) ecosystem. It's compressible and structured, making it an efficient storage format that can be queried "at rest" with no external services. In this talk, we'll walk through the specification itself, its history, and where it's going. The talk will include: - A roadmap for the specification - Discussion and comparison of tooling for reading and writing - Demonstration of querying stac-geoparquet at rest via DuckDB with STAC API parameters - Performance metrics relative to database-backed APIs and services - Analysis of the use of stac-geoparquet at huge (planetary) scales, including transaction support PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/9E77XF/ Lake Audubon Pete Gadomski PUBLISH DHFN7K@@talks.staging.osgeo.org -DHFN7K Remote Sensing for Plants that are Hard to See en en 20251104T133000 20251104T140000 0.03000 Remote Sensing for Plants that are Hard to See Plants that grow on California’s beaches are surprisingly hard to detect with traditional remote sensing methods. Plants are small and patchy (< 1 meter wide). A beach typically has a dozen or so different plant species. They are usually silvery in color (highly reflective), not green, and if they are green, they’re sticky so they are covered in sand. It’s difficult to distinguish small, sand colored plant pixels from actual sand. To make matters worse, the sand they live on shifts daily so the physical structure of their habitat is not stable or the same from day to day. This means your imagery needs to be from the same day as your elevation data or nothing will match up topographically. What’s a dune researcher to do? In this talk, I’ll explore some avenues for working with this difficult ecosystem. I will try to not only tell the plants from the sand, but tell the plants apart from each other using aerial imagery and elevation data using R’s spatial packages. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/DHFN7K/ Lake Audubon Michele Tobias PUBLISH XHYGC7@@talks.staging.osgeo.org -XHYGC7 Cloud Optimized Shapefiles en en 20251104T140000 20251104T143000 0.03000 Cloud Optimized Shapefiles Imagine a world where you could take a bounding box, and with a few quick look ups do a web request to download just the rows of a shapefile that are within that bounding box and then also do a separate request for the properties since that is stored in a separate file. You no longer have to imagine because that world, is the world you live in. Join us for an exciting talk well suited for any person interested in the minutia of how obscure geospatial files formates and obsolete databate files are laid out internally. Furthermore scintillating discussions of geo indexing strategies will also take place. This is THE talk to attend if you are passionate about sidecar files. Cat pictures will be provided. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XHYGC7/ Lake Audubon Calvin Metcalf PUBLISH QPVXUC@@talks.staging.osgeo.org -QPVXUC (Re)Making Cirrus: Five Years Building a Data Orchestration Framework en en 20251104T143000 20251104T150000 0.03000 (Re)Making Cirrus: Five Years Building a Data Orchestration Framework Cirrus is an open source, cloud-native framework for orchestrating geospatial data pipelines built using the concept of STAC (SpatioTemporal Asset Catalog) workflows. It provides a flexible and modular approach to deploying and managing serverless pipelines in AWS via python components and a Terraform-based deployment mechanism. Cirrus enables scalable, repeatable data processing workflows in the cloud, and is designed to help teams transform, validate, and catalog geospatial data in STAC-compliant formats at scales both large and small. Over the past five years, cirrus has evolved from a directory of loosely-organized bits of configuration and components built on top of the Serverless Framework to a robust, open source cloud-native data pipeline management system. In this talk, I’ll share my journey maintaining and evolving cirrus from what I inherited to its current state, and lessons I’ve learned along the way. Together we’ll explore cirrus’ origins and the original architecture and its challenges. We’ll examine the decision to shift away from duplicating deployment code via the configuration merge system of the first cirrus CLI, and what benefits and pitfalls that brought along with it. We’ll trace some of the tooling and ideas that spun out along the way, like stac-task and swoop. Finally, we’ll look at the version 1.0 release’s move away from Serverless Framework and the decoupling of the deployment logic from the codebase, the new cirrus Terraform module, and how this 1.0 release has prompted the reconsideration of what actually constitutes cirrus now. Whether you're maintaining your own internal tooling, building cloud-native data processing pipelines, or just trying to keep an open source project healthy through shifting technical landscapes, this talk will offer practical insights drawn from real-world experience. We'll cover the technical decisions, tradeoffs, and lessons learned—especially those relevant to anyone maintaining cloud-native tooling in a fast-moving landscape. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/QPVXUC/ Lake Audubon Jarrett Keifer PUBLISH BVW8CM@@talks.staging.osgeo.org -BVW8CM Introducing geospatial support in Apache Iceberg en en 20251104T153000 20251104T160000 0.03000 Introducing geospatial support in Apache Iceberg This talk explores Apache Iceberg's new native geospatial support (Iceberg Geo), tackling challenges in large-scale spatial data management. It covers Iceberg Geo's development, design, and goals, highlighting its impact on both geospatial and Iceberg communities. For geospatial users, it brings transactions, time travel, and schema evolution for spatial data. For Iceberg, it expands its use to complex spatial analytics. A live demo with Sedona and Spark will showcase geospatial data manipulation and querying. Future plans include enhancing Iceberg Geo and unifying geospatial/non-geospatial data processing. The session offers a deep dive into Iceberg Geo's capabilities and roadmap, emphasizing its role in advancing geospatial data management. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/BVW8CM/ Lake Audubon Matthew Powers PUBLISH 9CTQWA@@talks.staging.osgeo.org -9CTQWA Geophysics in the Cloud en en 20251104T160000 20251104T163000 0.03000 Geophysics in the Cloud Much geospatial analysis is devoted to natural or built environments on the Earth's surface. However, it is equally important to understand subsurface processes that affect these environments, such as earthquakes, landslides, and tsunamis. EarthScope collects and archives global seismic and geodetic data and is moving the archive from on-prem data centers to the cloud in Analysis Ready, Cloud Optimized (ARCO) formats. In addition to providing free access to the data, EarthScope is deploying GeoLab, a JupyterHub-based environment to support data analysis. GeoLab enables the integration of subsurface analysis with traditional geospatial analysis. For example, a researcher can combine fire burn scars from satellite imagery with soil moisture measurements from GNSS (via interferometric reflectometry) as inputs into a landslide model. This presentation demonstrates how cloud-based platforms and ARCO data can combine disparate data sets to enhance analytic processes. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/9CTQWA/ Lake Audubon Sophia Parafina PUBLISH W9MNNP@@talks.staging.osgeo.org -W9MNNP Making online maps accessible: an open source approach en en 20251104T163000 20251104T170000 0.03000 Making online maps accessible: an open source approach Over the past decade, GreenInfo Network has developed a series of open source tools to help the California Department of Parks and Recreation with funding decisions. These tools have been used to direct almost $1.3 billion in park funding to communities that most need investments in new parks. We built these tools using a range of open source software, including PostgreSQL/PostGIS, GDAL/OGR, Leaflet, Django and various JavaScript and Python libraries. We recently overhauled the website hosting these tools, ParksforCalifornia.org, to meet accessibility standards. By far, the biggest accessibility challenges we faced were with four data-driven map applications that display maps, data, and charts on parks, demographics, and park access. To be fully accessible, all maps and graphics must be keyboard accessible, meet standards for color contrast and color blindness, and all data must be presented in a comparable way to users of assistive technology such as a screen reader. We approached accessibility remediation of these tools in four discrete steps: Visual design phase, design review by an accessibility consultant, build phase, and final review of built products. We briefly describe these steps below and discuss what we learned through this process. The design phase involved reviewing and revising the existing versions of the tools to make improvements in three areas: simplifying and improving the workflow of the tools, updating colors on the maps and graphics to ensure they meet standards for color contrast and color blindness, and improving information hierarchy and the presentation of search results. There are significant design challenges in each of these areas. Maps with more than two or three colors, for example, often must employ borders and hatch patterns to ensure sufficient contrast. Another challenge is that maps must provide text alternatives for the visual information they convey, in figure captions or elsewhere on the page. Complete text descriptions of detailed map features can be difficult at scales larger than a neighborhood or city, which requires us to significantly rethink and overhaul how we present maps and statistics, e.g. lists of parks, that can potentially cover the entire state of California. The next step was to iterate on the design mockups with an accessibility consultant and the client, to ensure our proposed remediations were both accessible and continued to meet the original intent of the tools. Next was the build phase. This included writing new functions to calculate the proportions of important features visible in the client-side map, areas of parks and disadvantaged communities for example. These are reported back to the front end as the user interacts with the map, and are printed in dynamic captions and text areas below the maps. In certain cases, we also enumerate individual parks and provide these as a paginated list, as an alternative to the prior, less accessible method, where individual parks could only be queried through direct map click interaction. The final phase involved an iterative review of the built products by the accessibility consultant to ensure accessibility compliance. Through this process, we learned a number of important lessons that we think are generally helpful and applicable to any effort to make maps and data more accessible. The first of these is the need to look carefully and critically at each map and really understand who it is for, what it means, and what it is most fundamentally expressing, so you can effectively present it as either a map or as text. While people are increasingly experimenting with using artificial intelligence for translating visual map data to text, we found this to be unnecessary. Through this process, we were able to identify a relatively short list of key attributes that the map is conveying, and each of these can be effectively expressed as text, albeit with some work to make this dynamically available as the user pans or zooms the map, or selects a new area. An interesting and unexpected result of this process was the realization that improvements made for “accessibility” simultaneously make these products easier to use, understandable, and useful for users of all abilities. The very definition of “everybody wins”. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/W9MNNP/ Lake Audubon Tom Allnutt Dan Rademacher PUBLISH XXRWZV@@talks.staging.osgeo.org -XXRWZV Building the Global Spatial Data Infrastructure: Standards, Software, and Community en en 20251104T103000 20251104T110000 0.03000 Building the Global Spatial Data Infrastructure: Standards, Software, and Community The geospatial community has long dreamed of a connected world where spatial data flows freely between systems and organizations. Today, that vision is becoming reality through the adoption of open standards and the collaboration between open-source and proprietary platforms. This session explores how the modern spatial data infrastructure (SDI) leverages OGC standards like WFS 3.0/OGC API Features, GeoRSS, DCAT, and others to create a truly interoperable ecosystem. We'll examine real-world examples of organizations using mixed technology stacks - from PostGIS and GeoServer to ArcGIS Hub and OpenStreetMap - to share and consume standardized geospatial data. Learn how the community contributes to this global network and how you can participate using your preferred tools and workflows. Key topics: - Evolution of spatial data infrastructure from siloed systems to open networks - Deep dive into modern OGC standards and their practical implementation - Case studies of organizations successfully mixing open-source and commercial solutions - Best practices for contributing to and consuming from the global SDI This presentation will include practical code examples and architectural patterns that attendees can apply in their own organizations. Whether you're using FOSS4G tools, commercial software, or both, you'll learn how to be part of the growing open spatial data ecosystem. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XXRWZV/ Lake Thoreau Andrew Turner PUBLISH V7Z3RV@@talks.staging.osgeo.org -V7Z3RV Open Standards and FOSS4G for Interoperably Integrating Geospatial Sensors en en 20251104T110000 20251104T113000 0.03000 Open Standards and FOSS4G for Interoperably Integrating Geospatial Sensors The Open Geospatial Consortium’s new OGC API - Connected Systems Standard enables the interoperable integration of sensing systems of all kinds, There are a variety of FOSS4G implementations of this new standard that will be covered in this implementation. There will be a focus on OpenSensorHub (www.OpenSensorHub.org) and OSH Connect client APIs in various coding languages. This will cover Edge, Fog and Cloud deployments. There will also be a discussion of OSH integrations with the TAK Family of Systems, a FOSSG platform used by militaries, emergency services, governments and companies around the world. And, there will be discussions of OSH integrations with the open source Cesium ecosystem and its implementation of the OGC 3DTiles standard. The presentation will provide insights on how to integrate all manner of sensors and sensing systems (including IoT, drones and robots (think autonomous UxS), satellites, control systems and platforms across space, air, land, sea, cyber and electromagnetic domains within a common spatio-temporal framework - integrating with various 2D+T and 3D+T FOSS4G geospatial applications of different frameworks. The presentation will provide tips and tricks to FOSS4G developers on how to rapidly integrate such systems into a variety of common environments including OpenLayers, Leaflet, Mapbox, Deck.gl, Esri, Cesium, and the like. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/V7Z3RV/ Lake Thoreau Christopher Tucker PUBLISH EX9N97@@talks.staging.osgeo.org -EX9N97 Cataloging USACE Models in the Cloud: A STAC Experiment en en 20251104T113000 20251104T120000 0.03000 Cataloging USACE Models in the Cloud: A STAC Experiment The SpatioTemporal Asset Catalog (STAC) provides a standardized way to catalog and access geospatial data, making it easier for developers and researchers to find, share, and use various types of geospatial data. Increasingly, STAC is used by satellite providers to streamline access to their data, improve interoperability, and support cloud-native geospatial workflows. In a potentially controversial use case, we have been using STAC to catalog the underlying data that collectively represents environmental models, in particular hydrologic and hydraulic models. These models store geospatial, parameters, and time series data in a variety of file formats that are not cloud optimized, not conformant with open specifications, and may only be useable when opened in the model software itself. The provenance and data lineage for much of the foundational data stored in these files does come from traditional data formats (e.g. vector and raster data), but the metadata associated with source data is often lost. The model software is developed by the US Army Corps of Engineers, and while the source code is not open, it is free and is arguably the most widely used software in the world for inland flood hazards studies. While STAC is not a perfect fit, its community adoption and ecosystem of tools provide a uniquely adaptive and ready-set-go solution for cataloging models. This talk will address challenges of data management for USACE flood models—designed for the desktop—in the cloud, and how STAC provides a short term, interoperable solution for data cataloging, provisioning, and wrangling. Topics will include challenges of creating standardized—asset heavy—items, provenance linking, scaling probabilistic models, and operationalizing STAC into tools and workflows that support model development and execution. Tradeoffs, wins and losses, and some thoughts on what’s next moving forward will be included. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/EX9N97/ Lake Thoreau Seth Lawler Abdul Raheem Siddiqui PUBLISH SFMEHC@@talks.staging.osgeo.org -SFMEHC Simplifying the Retrieval of Geospatial Open Data en en 20251104T130000 20251104T133000 0.03000 Simplifying the Retrieval of Geospatial Open Data Open geospatial datasets, including modern formats like geoparquet stored on cloud platforms, offer tremendous value but remain challenging to access for users without specialized technical knowledge of S3 storage systems and REST APIs. This accessibility barrier prevents many potential users from leveraging valuable spatial data resources. Platforms like Carto and Fused have emerged as new cloud-driven solutions, offering intuitive interfaces that abstract away the technical complexities of data retrieval while providing robust analytical capabilities. These tools democratize access to geospatial data through streamlined workflows and user-friendly design. However, these platforms are opaque and costly. Open source solutions provide equally powerful alternatives via tools familiar to many users, like QGIS, R, and Python. These platforms can match the functionality of premium options while offering greater flexibility and customization. Open source geospatial packages provide integrations designed specifically for streamlined data retrieval, which eliminate the need for users to manually navigate complex APIs and storage systems. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/SFMEHC/ Lake Thoreau Ted Banken PUBLISH YGLW3D@@talks.staging.osgeo.org -YGLW3D OSM US Trails Stewardship Initiative: Open Data Meets Responsible Recreation en en 20251104T133000 20251104T140000 0.03000 OSM US Trails Stewardship Initiative: Open Data Meets Responsible Recreation As hikers in the US have come to rely on mobile applications to navigate our public lands, visitors can be led to dangerous or environmentally sensitive areas when these apps include unofficial and unmaintained trails; and much of that data comes from OpenStreetMap (OSM). In 2021 OSM US formed the Trails Stewardship Initiative (TSI) out of the urgency to improve the safety and protection of public lands. Since then, OpenStreetMap US and its partners have been working on building the relationships, technical requirements, and initial mapping workflows to support broader national efforts. The TSI brings together volunteers, land managers and app developers to improve trail data in OSM and address how third-party applications (AllTrails, onX, etc) use and visualize OSM data. These efforts enable navigation apps to better display OSM trail data, improving equitable access to the outdoors and the public's ability to understand and plan for the true nature of a trail system, while protecting our sensitive ecosystems. Improving trail data available to third-party apps in OSM provides a way for outdoor spaces to be more accessible to everyone, contributing to a more equitable, responsible, and safe outdoor experience. By involving locals in data stewardship, a sense of ownership and responsibility is fostered among those who cherish the outdoors. This talk will share the challenges, efforts and successes of the OSM US Trail Stewardship Initiative and the community collaboration behind it. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YGLW3D/ Lake Thoreau Maggie Cawley Jake Low PUBLISH 78NNYH@@talks.staging.osgeo.org -78NNYH Open Kentucky Imagery and Elevation Data en en 20251104T140000 20251104T143000 0.03000 Open Kentucky Imagery and Elevation Data Approximately six years ago, the KyFromAbove program encountered a dilemma because browsers were no longer FTP-friendly. For decades, the program had provided all its data free for public consumption via an FTP server. These data were all compressed/zipped archives. We quickly transitioned to Box.com as an alternative to serve this data, but this met neither our needs nor our consumer needs. Two years ago, we were fortunate to be accepted into AWS Open Data Sponsorship Program. This Program assists KyFromAbove by supporting the storage and distribution of all open data assets and exposing them in the Registry of Open Data. We quickly worked to download our data from Box to convert it into cloud-friendly formats, mainly Cloud-Optimized Geotiffs and Cloud-Optimized Point Clouds. Since transitioning, we achieved a major milestone in acquiring 3" leaf-off ortho and oblique imagery statewide. Without the open data program, storing and distributing over 300 Terabytes of imagery and elevation data would quickly become costly. But the true benefit of exposing KyFromAbove data on an open data platform and adopting cloud-friendly formats, is the data is now more usable and discoverable than ever. People can use tools such as STACs (SpatioTemporal Asset Catalog) to spatially and temporally query, Titiler to create and serve dynamic mosaics, GDAL, PDAL, and the list goes on. In this talk, I will provide an overview of how we transitioned our data into the Open Data Registry. Finally, I will highlight some examples and tutorials demonstrating how users can easily consume our data in various cloud-friendly formats and open and free software. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/78NNYH/ Lake Thoreau Ian Horn PUBLISH GW89SP@@talks.staging.osgeo.org -GW89SP Microsoft’s Planetary Computer: Building a Planetary-Scale Data Platform en en 20251104T143000 20251104T150000 0.03000 Microsoft’s Planetary Computer: Building a Planetary-Scale Data Platform Since its launch in 2020, Microsoft’s Planetary Computer has grown into one of the world’s largest geospatial platforms, hosting a data catalog with over 50 petabytes of open-access earth observation and environmental data. Accessed billions of times each month, the Planetary Computer helps researchers, policymakers, and businesses develop new solutions to climate, biodiversity, and sustainability challenges. This session will explore three key areas: (1) what the Planetary Computer offers today, (2) how we built and scaled it, and (3) what’s new and what’s next for the FOSS4G community. For those that are new to the Planetary Computer, this session will open with a brief tour of the Planetary Computer’s Data Catalog and APIs. We will showcase the diversity of open-access datasets within the Planetary Computer and highlight how anyone can use its APIs to perform planetary-scale analyses. Next, this session will cover the programmatic and technical challenges we had to overcome to build and maintain a planetary-scale data platform. We’ll discuss the importance of collaboration and community engagement in overcoming these challenges. We’ll highlight why building upon and contributing to community standards, such as the SpatioTemporal Asset Catalog (STAC) specification, continues to be central to our strategy. Finally, we’ll share updates on new datasets, improved accessibility, and how we’re enabling tighter collaboration between geospatial data scientists, engineers, and business users. We’ll also spotlight emerging standards and technologies that we’re actively evaluating and encourage the FOSS4G community to explore as well. Whether you’re a developer, data scientist, or decision-maker, this session will offer practical insights into how you can leverage the Planetary Computer in your own work and how we can collectively advance open geospatial technology to meet planetary-scale challenges. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/GW89SP/ Lake Thoreau Taylor Corbett PUBLISH WJJRCU@@talks.staging.osgeo.org -WJJRCU From Esri to Open Source: A Practical, Hybrid Approach en en 20251104T153000 20251104T160000 0.03000 From Esri to Open Source: A Practical, Hybrid Approach This talk will describe a hybrid geospatial workflow combining ArcGIS Online with a self-hosted FOSS backend built for a small municipality in Ohio (now devoid of any GIS staff). We will outline how we came to this point, the tools involved, and some of the roadblocks met along the way. The stack consists of a custom Mapbox GL-based frontend, backed by PostgreSQL/PostGIS via pg_featureserv and pg_tileserv, and currently Esri FieldMaps for data collection. To sync from AGOL to Postgres we use a collection of python-based tools (RESTerville). The majority of the backend FOSS stack is deployed via Docker, with the object-based storage, static raster tile server, and frontend managed separately. A few of the unique features of the backend stack include syncing feature services from AGOL to Postgres including attachments, property search and city service information via pg_featureserv, and pg_tileserv for about everything else. On the frontend we have deployed a simple print feature used extensively by city officials, a sewer system trace feature, saved map state, and customizable feature filters. The result is a fast, mobile-friendly mapping app used throughout the city. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/WJJRCU/ Lake Thoreau Malcolm Meyer PUBLISH EWLUGM@@talks.staging.osgeo.org -EWLUGM Development of Online Mars Viewshed Analysis Tool en en 20251104T160000 20251104T163000 0.03000 Development of Online Mars Viewshed Analysis Tool According to Kidner et al. (2001), “the viewshed is defined as the complete visible area of a viewing location”. Therefore, viewshed provides crucial information when planning Mars exploration using rovers or any other platforms. Many commercial or open source geospatial software provides viewshed analysis tools. Especially, GDAL has its own viewshed module – gdal_viewshed. Gdal_viewshed uses the method of Wang et al’s (2000) with the Digital Elevation Model (DEM) as input data. The delineation of viewshed is a quite straightforward task in a desktop environment if (1) GIS software is available; (2) DEM data is properly prepared; and (3) the performance of hardware is strong enough. However, it takes painstakingly long time often to download the DEM if the coverage area is large. Also, running viewshed delineation takes longer time if the tool runs with large size of input DEM on a consumer level desktop machine. Therefore, the research objectives is to build a web-based Mars viewshed delineation GIS application, which (1) prepares input Mars DEM data automatically; and (2) to delineate viewshed on powerful research grade server machine. The users of this tool won’t need to (1) download large size of Mars DEM data; or (2) use high performance research grade workstation. The only requirement for the users will be the access to the web browsers and internet connection. Currently, the prototype of viewshed delineation tool is being developed in Geospatial Cyber Infrastructure (GCI) at Michigan Technological University (Michigan Tech). The geospatial cyber infrastructure (GCI) was built in Ubuntu Server. Django was used for web framework of the GCI since Django is known to be secure, scalable, versatile and fast to develop with (Django Software Foundation, 2025). Since Django requires database by default, PostgreSQL was used as database software considering future integration of PostGIS. Mapserver was used to serve Mars base layer (Mars Viking Colorized Global Mosaic 232m V2) (Williams, 2018). The GDAL and OGR were used as geoprocessing toolkits for each raster and vector data. Python was used as a server side language since it supports Django and GDAL/OGR. In the client side, OpenLayers was used as mapping toolkit to add input observation points and output viewshed areas. In the prototype of viewshed delineation tool, users can mark multiple observing points on the base layer and can run viewshed delineation. These points can be assumed as observation points from exploration rovers. Finally, total viewable areas from observing points are displayed as layers of polygons. Currently, two types of DEMs are used as input data in the tool. The first input DEM is Mars HRSC MOLA BlendDEM Global 200m (Fergason et al., 2018), which was used for delineation viewshed for the entire surface of Mars. Since this DEM enables viewshed delineation for the entire Mars surface, the resolution of DEM is not quite high. Therefore, the result using this DEM can be used as viewshed area for small map scale (larger area). To provide higher resolution DEM, the clipped data from Mars MSL Gale Merged DEM 1m (Calef III et al, 2016) was added in the viewshed delineation tool as the first step. Since the resolution of this DEM is quite high, high precision result is expected for smaller area (larger map scale). As project progresses, additional high resolution Mars DEM data will be added in the Mars viewshed delineation tool to support high precision results in more exploration areas. In the case of high resolution DEM, the large data size tends to slow down the viewshed delineation process. Therefore, parallel computing (multithreading) was used to increase speed of viewshed delineation for multiple viewpoints. The output raster from viewshed delineation tool is converted to polygon vectors using gdal_polygonize.py. As project evolves, user interface and output delineation results will be refined to provide more sophisticated user experience. In addition, more open source geospatial or software technologies will be integrated into GCI to add more geospatial analysis/processing capabilities. Currently, the tool is running in local development environment and being tested in a beta website served in Michigan Tech. References 1. Calef III, F.J. and Parker, T., 2016, MSL Gale Merged Orthophoto Mosaic, PDS Annex, USGS, https://astrogeology.usgs.gov/search/map/mars_msl_gale_merged_dem_1m 2. Django Software Foundation (2025). Django Overview | Django, https://www.djangoproject.com/start/overview/ 3. Fergason, R. L, et al. (2018). HRSC and MOLA Blended Digital Elevation Model at 200m v2. Astrogeology PDS Annex, USGS https://astrogeology.usgs.gov/search/map/mars_mgs_mola_mex_hrsc_blended_dem_global_200m 4. Kidner, D. B., et al. (2001). Visibility analysis with the multiscale implicit TIN. Transactions in GIS, 5(1), 19-37. 5. Wang, J., et al. (2000). Generating viewsheds without using sightlines. Photogrammetric engineering and remote sensing, 66(1), 87-90. 6. Williams, D. R. (2018). Viking Mission to Mars. https://nssdc.gsfc.nasa.gov/planetary/viking.html PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/EWLUGM/ Lake Thoreau Jae Sung Kim PUBLISH VERGBR@@talks.staging.osgeo.org -VERGBR From proprietary to open: smarter disaster forecasting with geospatial tools. en en 20251104T163000 20251104T170000 0.03000 From proprietary to open: smarter disaster forecasting with geospatial tools. Disaster forecasting has traditionally depended on complex, one-size-fits-all models. But just as medicine has evolved toward personalized care, there's a growing movement to customize forecasting tools to the specific needs of emergency response agencies. By leveraging historical geospatial data, uncovering key spatial-temporal patterns, and focusing on real-world decision-making, agencies can build lightweight, scalable forecasting models that are both practical and impactful. This talk shares how these methods were first developed using ESRI's powerful geospatial platform and then transitioned to free and open-source software (FOSS) tools. We’ll explore what’s gained—and what’s challenged—when moving from proprietary to open: from cost savings and flexibility to integration hurdles and support trade-offs. Whether you're working in disaster response, urban planning, or public safety, you'll come away with actionable insights on building smarter, mission-driven forecasting systems that fit your agency’s unique needs. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VERGBR/ Lake Thoreau Aaron Kelley PUBLISH PPRFPP@@talks.staging.osgeo.org -PPRFPP What's going on? An introduction to diagnosing PostgreSQL systems. en en 20251104T103000 20251104T110000 0.03000 What's going on? An introduction to diagnosing PostgreSQL systems. This is an introduction to understanding what your PostgreSQL database management system is doing by using pg_top and pg_systat open source tools. These tools provide a simple way for identifying the PostgreSQL processes using the most the processor, memory, or storage resources. They also allow reviewing many of the PostgreSQL statistics tables to spot things like the most heavily accessed tables and indexes. While pg_top and pg_systat are terminal based tools, the concepts presented here can be applied to other tools whether they are terminal, graphical, or Web based. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PPRFPP/ Lake Fairfax Mark Wong PUBLISH Y9VWWN@@talks.staging.osgeo.org -Y9VWWN The Maze of Postgres Options en en 20251104T110000 20251104T113000 0.03000 The Maze of Postgres Options Slides are at https://momjian.us/main/presentations/open_source.html#maze PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/Y9VWWN/ Lake Fairfax Bruce Momjian PUBLISH VVGXJP@@talks.staging.osgeo.org -VVGXJP PostGIS What's New en en 20251104T113000 20251104T120000 0.03000 PostGIS What's New PostGIS releases once a year, and each year brings a few notable new features or enhancements. The projects surrounding PostGIS, like GEOS, Proj, and PostgreSQL are also advancing, and this talk will highlight notable new features in those projects too. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VVGXJP/ Lake Fairfax Paul Ramsey PUBLISH 9UXGRD@@talks.staging.osgeo.org -9UXGRD PostGIS performance hacks en en 20251104T130000 20251104T133000 0.03000 PostGIS performance hacks In this talk I'll go over * Spatial index in combination with other indexes. * Materialized views * Writing performant queries * Using processing functions to minimize weight of geometries PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/9UXGRD/ Lake Fairfax Regina Obe PUBLISH ARHG3W@@talks.staging.osgeo.org -ARHG3W Introduction to pgRouting en en 20251104T133000 20251104T140000 0.03000 Introduction to pgRouting pgRouting is a network routing PostgreSQL extension that complements PostGIS. You'll learn how to use pgRouting to plan train, biking, walking route problems and how to use it to plan the location of your next store as well as non-spatial challenges. You'll learn the following: 1. Loading data 2. Planning routes 3. Driving distance and service area optimization 4. Contracting graphs for better performance PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/ARHG3W/ Lake Fairfax Regina Obe PUBLISH DEDLRF@@talks.staging.osgeo.org -DEDLRF Will Postgres Live Forever? en en 20251104T140000 20251104T143000 0.03000 Will Postgres Live Forever? Slides are at https://momjian.us/main/presentations/open_source.html#forever PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/DEDLRF/ Lake Fairfax Bruce Momjian PUBLISH HAYXUE@@talks.staging.osgeo.org -HAYXUE Data Horizons With Postgres en en 20251104T143000 20251104T150000 0.03000 Data Horizons With Postgres Slides are at https://momjian.us/main/presentations/extended.html#horizons PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/HAYXUE/ Lake Fairfax Bruce Momjian PUBLISH LS7DVF@@talks.staging.osgeo.org -LS7DVF Optimizing Complex Webmaps using Vector Tiles, Cacheing, and PostGIS en en 20251104T153000 20251104T160000 0.03000 Optimizing Complex Webmaps using Vector Tiles, Cacheing, and PostGIS In 2018, UMD's Center for Advanced Transportation Technology (CATTLab) released its first iteration of the Trip Analytics project. This web application was designed for use by transportation planners and operations managers to help them analyze both recent and historical traffic trends, using licensed vehicle trip data from providers such as Inrix and Geotab. The application guides users through the creation of a geospatial query using a webmap, which is then submitted to the back-end for processing. The team has made continual improvements to the front-end user experience by incorporating new open source software libraries that have become more prevalent since the project’s instantiation. In this talk we will present our changes and lessons learned. The following section describes briefly a few of the changes we intend to discuss. 1. Transition to Vector Tiles: When this project was first developed, leaflet.js was the recommended webmapping library. As such, our development focused on sending geojson back and forth between the PostGIS database and the front-end. We primarily work with political boundaries at various resolution levels (country, state, county), which can get quite complex. Moving over to Vector Tiles allowed us to display levels of data previously unfeasible - Where we initially had to limit users to viewing boundaries only within a smaller selected region of interest, we can now display the entire country’s worth of data. 2. Pg_tileserv: In the past year, we have started incorporating pg_tileserv into our project, in order to reduce our own development costs. Before this, we worked with a custom tile server developed in-house. However, using pg_tileserv allows our team’s developers to iterate more quickly as we can access desired geospatial columns and implement custom MVT functions with minimal friction. 3. Moving geospatial processing from turf.js to PostGIS: Our original development efforts focused on developing a rapid prototype. As such, much of our geospatial processing was baked into the front-end using turf.js. This worked initially, but pushing all of this work onto the browser was not scalable. One example of this is the calculation of time zones for a user’s selected region. When working only within the United States, it was trivial to calculate the intersection of 5 possible times for a user’s selection. Once we began working with global data sets, we realized we had to move this to the back-end in PostGIS. Now, the user’s selection ID is sent to the back-end, where a PostGIS function determines the intersection of the selection’s geometry with all global time zones. To further optimize the calculation, we have precalculated and stored the timezones for many popular regions of interest. 4. Redis Cacheing and Tanstack Query: Pushing processing to PostGIS didn't eliminate all processing speed concerns. There’s no way to avoid that things such as bounding boxes and intersections take time to calculate. Proper use of these two libraries allowed us to avoid repeating work. 5. The value of proper DB design and learning postgresql: During the development process, we went through multiple methods for storing user’s query data. We’ll describe how we ended on our current method for storing user defined areas in a custom table, and how this table facilitates some of our development processes, as well as point out some pitfalls where we did not make of use of the table initially (and really wished we had later on). We’ll also talk about the benefits we found as our front-end developers became more familiar with PostGIS functions. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/LS7DVF/ Lake Fairfax Annie Cartas PUBLISH F9788R@@talks.staging.osgeo.org -F9788R Dynamic Aggregations With pg_tileserv en en 20251104T160000 20251104T163000 0.03000 Dynamic Aggregations With pg_tileserv Mapping applications sometimes need to visualize millions of data points without sacrificing performance or user interactivity. In this talk, we will share a real-world use case where PostgreSQL, PostGIS, and pg_tileserv were combined to create dynamic, filterable aggregation layers. Using function layers, we built a simple stack that supports on-the-fly visualizations, allowing users to explore data with responsive, interactive aggregations. I’ll walk through the design choices, including hex bins, square grids, and dot grids, and discuss the benefits of dynamic filtering for real-time data exploration. Whether you're building a data-heavy GIS application or looking to enhance your digital cartography toolkit, this talk will provide practical insights and code snippets to tackle similar challenges. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/F9788R/ Lake Fairfax Brad Andrick PUBLISH PGPNXR@@talks.staging.osgeo.org -PGPNXR Spatial SQL Birds of a Feather en en 20251104T163000 20251104T170000 0.03000 Spatial SQL Birds of a Feather This is a birds of a feather for all those who love to mix Spatial with SQL. Spatial SQL is supported in a number of databases and products. Some common ones are PostgreSQL / * PostGIS, pgRouting, MobilityDb * DuckDb * Apache Sedona * SQL Server * MySQL Come find out all the ways you can employ Spatial SQL in your work. PUBLIC CONFIRMED Birds of a Feather https://talks.staging.osgeo.org/foss4g-na-2025/talk/PGPNXR/ Lake Fairfax Regina Obe PUBLISH 7LDTBG@@talks.staging.osgeo.org -7LDTBG When LLMs Meet GIS: Reliable Open Source Geospatial AI en en 20251104T103000 20251104T110000 0.03000 When LLMs Meet GIS: Reliable Open Source Geospatial AI Many organizations are working to harness LLMs for geospatial understanding, but face significant challenges including hallucinations, non-determinism, and the "black box" nature of these models. The geospatial domain introduces additional complexity, as LLMs are primarily designed for text-based tasks rather than spatial reasoning. This talk introduces two open source libraries we've developed to address these challenges: - https://github.com/Element84/e84-geoai-common/ - A foundation for geospatial LLM tasks that enables conversion of natural language to structured Pydantic models, supports batch processing, and includes robust evaluation frameworks. - https://github.com/Element84/natural-language-geocoding/ - An innovative library that converts natural language descriptions like "within 2 km off the coast of Maui, west of Kahului" into precise polygons. We'll explain why we built custom libraries instead of adapting existing frameworks. While solutions like LangChain and DSPY offer powerful features, they often require contorting your code to fit within complex, deeply nested class hierarchies. Our approach allows you to leverage LLMs while effectively containing their non-deterministic behavior, enabling natural language capabilities without restructuring your entire development approach. Our libraries specifically address the ambiguity in spatial language while maintaining the precision geospatial applications demand. The e84-geo-ai-common library provides foundational tools for geospatial AI/ML work, with special emphasis on LLM integration. We'll demonstrate its core components: - Base LLM classes with clean abstractions that simplify implementation details - Implementations for multiple models with batch support - Utilities for processing natural language into structured Pydantic models - Enhanced geospatial utilities built on Shapely for specialized operations The natural-language-geocoding library represents a significant innovation in converting descriptive language into geometric representations. We'll showcase how it transforms expressions like "within 2 km off the coast of Maui west of Kahului" into precise polygons through: - Representation of spatial queries as directed computational graphs - Support for complex spatial operations including intersection, union, coastal features, borders, and more - A customizable geocoding database built from open data sources like Who's On First and Natural Earth - Handling of colloquial place references and geographic hierarchies Throughout the presentation, we'll emphasize our design philosophy centered on reliability. We'll share concrete examples of how we've addressed challenges through: - Strategic design choices that focus LLMs on their strengths (translating natural language to spatial operations) while using traditional geospatial libraries for geometric precision - Robust error handling patterns to manage edge cases in spatial descriptions - Comprehensive evaluation frameworks for measuring and improving accuracy PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/7LDTBG/ Lake Anne Jason Gilman PUBLISH UDTKXU@@talks.staging.osgeo.org -UDTKXU Scalable GeoAI: Building LLM Agents Using National Spatial Data Infrastructures en en 20251104T110000 20251104T113000 0.03000 Scalable GeoAI: Building LLM Agents Using National Spatial Data Infrastructures AI holds the promise of enabling government agencies to overcome the difficult challenge of improving efficiency despite deep budget cuts. Although LLMs excel at natural language processing and general reasoning, they lack the cross-sectoral, domain-specific, and up-to-date geospatial knowledge needed for decision support in improving disaster and crisis management localized responses, public health, business and economic insights, and security and defense applications. LLMs can discover networks of interrelated features for these purposes by leveraging the wealth of information provided by spatial data infrastructures. However, due to the incompatible representations of the same locations across systems, integrating by common geographies is prohibitively labor-intensive, especially when faced with staff reductions. For many years, we have treated data quality as an analytics problem, delegating dirty data to the data team for cleanup in the data warehouse or lake. This approach is not suitable for AI applications. GenAI applications operate in real time, making decisions on the fly. If the data is incorrect, incomplete, or poorly structured, AI will not rectify it. Instead, it will make erroneous decisions more rapidly. You can’t wait until the analytics layer to ensure data quality when AI agents need to reason, plan, and act in real-time. At FOSS4G NA 2024, we presented on the potential of spatial knowledge graphs (SKGs) to address limitations of LLMs with evolving domain-specific and spatial awareness. These SKGs, which are interoperable by location using machine-to-machine readable interfaces, can effectively manage changes over time. We initially developed an approach for managing dependencies and propagating change between interlinked spatial knowledge graphs within the health sector. However, this approach has since been adapted to serve NSDIs for two governments and disaster resiliency efforts. This approach has now been implemented and deployed to production for the U.S. Army Corps of Engineers. This was achieved through the implementation of a Geospatial Knowledge Infrastructure (GKI), which enables the on-demand integration of SKGs, allowing LLMs to respond to ad-hoc queries. We will demonstrate how to implement an SKG as a graph-based retrieval-augmented generation (GraphRAG) that sustainably captures the semantics of geoinformatics as text that an LLM Agent can leverage. Instead of requiring the costly process of model training, the SKG as a RAG can represent semantic geospatial relationships across entire networks of features in multiple domains and in real time, if necessary, to understand the downstream impact or cumulative effect of events of interest. We will also demonstrate how to write prompts for an LLM Agent to translate natural language questions into GeoSPARQL graph queries, which minimizes hallucinations. Accessing these generated queries provides traceability and can also be used to display answers on a map that correspond to the text response from the LLM. We will show how to display the individual features that comprise an aggregate question on a web map. For example, asking how many people would be affected by a flooding event can reveal the individual administrative boundaries that form the aggregate answer. The approach utilizes Apache Jena to implement the GraphRAG. The web map interface was developed entirely using open-source software components. Finally, we will cover some of the standards being discussed in the Open Geospatial Consortium Geospatial Semantics Domain Working Group to address current standards gaps. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/UDTKXU/ Lake Anne Nathan McEachen PUBLISH V3W87F@@talks.staging.osgeo.org -V3W87F Bringing it all together: Zarr, Dask, Knowledge Graphs, and LLMs en en 20251104T113000 20251104T120000 0.03000 Bringing it all together: Zarr, Dask, Knowledge Graphs, and LLMs In this talk, we describe our ongoing open source work building a system to which users can pose questions in natural language, such as “What are the trends in sea surface temperatures around near-coast gulf waters over the past several decades?” and get answers derived from real data that are not only correct and comprehensive, but also include detailed provenance information so that users may verify them. NOAA maintains thousands of datasets of various kinds (remote sensing, in-situ, derived etc.) across multiple domains (climate, weather, ecology, etc.) that are consumed by a wide variety of users (scientists, engineers, urban planners, etc.), but discovering, accessing, and using these datasets remains a significant challenge. This talk describes the work undertaken as part of the NOAA-funded “Study to Determine Natural Language Processing Capabilities with the NCCF Open Knowledge Mesh (KM/NLP)” BAA, which aims to study the feasibility of overcoming this challenge through the use of knowledge graphs and state-of-the-art Large Language Models (LLMs). Key aspects of our solution include: consolidating thousands (or hundreds of thousands) of NetCDF files into virtual datasets using Zarr, scaling computations on these datasets using Xarray and Dask, representing and querying metadata about datasets and variables through knowledge graphs, and using LLMs to translate plain-language user questions into graph queries and data transformations to derive answers. We highlight how this work is exciting because it takes the fruits of labor of the open source GIS community such as cloud-optimized data formats, cloud compute, metadata ontologies, and builds upon them to solve the “last mile” problem of letting users extract insights from data with minimal effort. We also discuss where these building blocks currently fall short and how they may be improved if we want to scale such solutions to even more – perhaps even ALL – datasets. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/V3W87F/ Lake Anne Adeel Hassan PUBLISH RN3D7Z@@talks.staging.osgeo.org -RN3D7Z Giswater 4. State of the art en en 20251104T130000 20251104T133000 0.03000 Giswater 4. State of the art The increasing complexity of urban environments, along with the growing challenges of climate change, urbanization, and regulatory demands, calls for a more rigorous, transparent, and efficient approach to managing critical water infrastructure. In this context, Geographic Information Systems (GIS) and network-based mathematical modeling tools have become essential—not only in the design phase, but also throughout operation and asset management. These tools provide the spatial intelligence and analytical power needed for informed decision-making about the structure, behavior, and evolution of drainage and sewerage systems. More than ten years after the launch of version 1.0, Giswater (www.giswater.org) is now used in multiple projects across the Americas and Europe. Today, water utilities powered by Giswater serve over 20 million people. These implementations demonstrate the feasibility of a comprehensive, long-term strategy for managing water supply, sewerage, and drainage networks using open-source technologies. This approach promotes transparency, adaptability, and cost-effectiveness, making advanced water management accessible even to municipalities with limited resources or technical capacity. During the design and planning phase, GIS platforms—especially those integrated with spatial databases like PostgreSQL/PostGIS—enable detailed analysis of terrain, land use, and existing infrastructure. Engineers and planners can model flow paths, determine optimal pipe slopes, and evaluate design alternatives. Simulation engines such as SWMM (Storm Water Management Model) for drainage systems and EPANET for water supply allow users to simulate network behavior under different conditions. Open-source tools like QGIS, combined with platforms like Giswater, connect spatial data, hydraulic models, and relational databases. This integration allows users to visualize and edit the network directly in GIS, run simulations, and assess system performance within a unified environment. Standardized formats also support collaboration among public authorities, private operators, and consultants. Design workflows include selecting materials, sizing conduits, and defining structures such as inspection chambers and retention tanks. All elements can be documented within the same ecosystem, ensuring consistency and traceability throughout the project lifecycle. In the operational phase, open-source GIS tools become even more critical. As new connections are added and assets age, the network evolves. A dynamic, up-to-date model is needed—one that reflects real-time changes, historical records, and future planning. Using PostgreSQL/PostGIS as the relational and spatial foundation, sanitation utilities can manage detailed inventories of network components, including attributes like material, diameter, installation date, inspection records, and maintenance history. This supports preventive and corrective maintenance plans, helping utilities prioritize interventions and optimize field operations. Hydraulic models also play a role during operation, allowing utilities to anticipate failures, evaluate the impact of blockages or overflows, and manage emergencies during extreme weather events. Integrated with GIS, these simulations help visualize system behavior and enable timely, data-driven responses. Monitoring and control can be improved through manual data input or integration with telemetry systems. Key performance indicators (KPIs) such as flow, capacity, and incident frequency can be tracked and visualized using open-source business intelligence dashboards. Perhaps the most impactful aspect of this initiative is its proof that all these capabilities are achievable using open-source software—without sacrificing quality, performance, or technical depth. The traditional barriers of costly proprietary licenses and vendor lock-in are removed, making it possible for even small municipalities to modernize their water infrastructure management. By adopting an open-source philosophy, this initiative encourages cost reduction, interoperability, and long-term sustainability. It enables knowledge sharing, community-driven development, and tool customization tailored to local needs. Ultimately, this project marks a step forward in the democratization of urban water infrastructure. It proves that professional-grade results are possible using accessible, open technologies—paving the way for a more inclusive, resilient approach to water management where every city, regardless of size or budget, can manage its infrastructure efficiently, intelligently, and transparently. source code: https://github.com/Giswater/ PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/RN3D7Z/ Lake Anne XAVIER TORRET PUBLISH G7UAJ3@@talks.staging.osgeo.org -G7UAJ3 SDI for water management at AyA in Costa Rica en en 20251104T133000 20251104T140000 0.03000 SDI for water management at AyA in Costa Rica At the core of the project was the consolidation of all spatial data into a centralized, enterprise-level geospatial database. This ensured consistent, high-quality data management and enabled real-time access and editing across departments and user profiles. Custom GIS applications were developed for both desktop and web environments to support the visualization, analysis, and management of infrastructure assets such as water distribution systems, sanitation, and urban drainage networks. The platform serves a wide range of users—from field engineers to decision-makers at headquarters. QGIS is used as the primary desktop GIS tool for spatial analysis and editing. On the web, QWC2 (QGIS Web Client 2) offers a lightweight, modern interface for map visualization and data querying. GeoNode functions as a spatial data catalog and publishing platform, promoting open data standards and facilitating both internal and public sharing of geospatial information. The core database architecture is based on PostgreSQL clusters (using Patroni and PgCat) with PostGIS extensions, enabling powerful spatial queries and seamless GIS integration. Data ingestion, transformation, and validation are largely automated with Python scripts and PyQGIS. Giswater serves as a key interface between GIS and hydraulic modeling tools such as EPANET and SWMM, supporting simulation and operational planning of water networks. To enhance monitoring and reporting capabilities, Apache Superset is used as a business intelligence tool. It allows the creation of interactive dashboards and KPIs that combine spatial and operational data, supporting transparency and data-driven strategic planning through real-time visualizations. The entire system infrastructure is containerized using Docker and managed through Docker Compose. This setup ensures high availability, modular architecture, and simplified maintenance. Security is reinforced through strong authentication mechanisms, including Single Sign-On (SSO) and two-factor authentication (2FA), protecting access to sensitive systems and data. Operational reliability is maintained through a comprehensive monitoring stack. Zabbix and Prometheus serve as core monitoring tools. Alloy collects logs, which are centralized in Loki. Alerting is handled by AlertManager and Zabbix, while Grafana provides dynamic visualization of metrics and logs. Together, these tools offer full visibility into system performance, availability, and resource usage, enabling both proactive and reactive issue resolution to maintain uninterrupted service. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/G7UAJ3/ Lake Anne XAVIER TORRET Sergio Baños Calvo PUBLISH A73VCA@@talks.staging.osgeo.org -A73VCA Smart Water Utilities: Chat with Your Water Utility Database en en 20251104T140000 20251104T143000 0.03000 Smart Water Utilities: Chat with Your Water Utility Database This session will demonstrate a working prototype of a natural language assistant for water utilities, built with open source tools and real-world data. Attendees will learn: How to structure water utility data (e.g., pipes, valves, hydrants) for spatial querying in your database How to use Langchain to build a natural language interface that interprets and runs SQL and spatial queries How this system can be deployed using Python I’ll also discuss lessons learned from working with small utilities in Germany, how open data standards help interoperability, and how these tools can democratize access to geospatial intelligence. This talk is ideal for GIS professionals, and public sector innovators interested in AI + geospatial solutions. It combines technical implementation with real-world relevance—and is open to collaboration and feedback from the FOSS4G community. Key Takeaways: - How to modernize water utility management using AI - Learn how water utility teams can interact with spatial databases using plain language, without needing SQL or GIS expertise. - Understand how this technology can be used to track assets, schedule maintenance, and respond to events like leaks or construction, all through conversational interaction. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/A73VCA/ Lake Anne Bernie Drahola PUBLISH KHG9XZ@@talks.staging.osgeo.org -KHG9XZ Unifying Access to Western Water Data Through OGC API - EDR en en 20251104T143000 20251104T150000 0.03000 Unifying Access to Western Water Data Through OGC API - EDR The Western Water Data Hub (WWDH) is an initiative designed to simplify access to water-related time series data across the Western United States by providing a unified, standards-based interface implementing the OGC API Environmental Data Retrieval (EDR) standard. A key goal of the project is to demonstrate how a real-world standards based API—like EDR—can streamline access to heterogeneous time series data from disparate systems in real time. Developed through a collaboration between the US Bureau of Reclamation, the Center for Geospatial Solutions, and the Western States Water Council, the WWDH integrates multiple data sources by leveraging pygeoapi with custom EDR plugins. This architecture enables users to access real-time data from a diverse set of existing APIs, through a consistent interface. A distinctive feature of the WWDH is its ontology abstraction layer, which facilitates the semantic resolution of parameters. By defining source specific vocabulary terms with ontologic matches in the Observations Data Models 2 (ODM2) vocabulary, the platform enables meaningful cross-source comparisons. Users can discover and query data based on standardized parameter names and units, even when underlying systems use inconsistent terminology. This session will discuss the merits of implementing EDR as well as share lessons learned from implementing it in environments with legacy APIs, including challenges around latency, caching, parameter harmonization, and semantic alignment. Attendees will also see live demonstrations of how to access and retrieve water data through the hub’s EDR API. This session will also cover both the technical implementation and practical benefits of the WWDH. It will highlight how standards-driven development with OGC API and semantic ontologies lower barriers to data interoperability and unlock new possibilities for water data integrations. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/KHG9XZ/ Lake Anne Benjamin Webb Colton Loftus PUBLISH 3LHPEN@@talks.staging.osgeo.org -3LHPEN DUSTCAST: AN ENSEMBLE ML MODEL FOR ARABIAN PENINSULA DUST CONCENTRATIONS en en 20251104T153000 20251104T160000 0.03000 DUSTCAST: AN ENSEMBLE ML MODEL FOR ARABIAN PENINSULA DUST CONCENTRATIONS This study presents DustCast, an ensemble machine learning (ML) model developed to forecast monthly atmospheric dust concentrations across the Arabian Peninsula (AP). Motivated by the increasing frequency and intensity of dust storms in the region and their associated adverse impacts on health, agriculture, and the environment, the model integrates multiple free and open-source meteorological and aerosol datasets, including ERA5 reanalysis, MERRA-2 aerosol diagnostics, and the Indian Ocean Dipole index. The methodology employs a heterogeneous parallel ensemble framework that combines four regression techniques: multiple linear regression (MLR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF), with weights assigned based on each model's performance as evaluated by root mean squared error (RMSE). Spatial aggregation using the H3 hexagon grid system facilitates efficient and precise data binning and analysis. Results indicate that MLR and RF exhibit superior predictive capabilities among the individual models on the surface, with the aggregated ensemble prediction achieving an RMSE of 0.00972 micrograms (µg/m3) and an R2 of 0.887, outperforming each base learner. When applied to the atmospheric column, DustCast derives the majority of the predictive contributions from DT and RF, with the ensemble prediction achieving an RMSE of 0.00550 milligrams (mg/m2) and an R2 of 0.984. The DustCast ensemble model captures seasonal patterns of dust mobilization across the AP. The model performs particularly well during the summer months (JJA) when the Shamal wind strongly influences sand and dust storms throughout the region. It also captures seasonal dust events associated with frontal systems and dynamic pressure gradients throughout the year. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/3LHPEN/ Lake Anne Christopher Ramos PUBLISH QA83HS@@talks.staging.osgeo.org -QA83HS Dengue Geospatial Prediction Tools for Epidemic Response and Resource Allocation en en 20251104T160000 20251104T163000 0.03000 Dengue Geospatial Prediction Tools for Epidemic Response and Resource Allocation Dengue fever is one of the most common and rapidly spreading arboviral diseases in the world, with major public health and economic consequences, especially in tropical and sub-tropical regions. The World Health Organization (WHO) reports that the global burden of Dengue increased eight-fold over the last two decades. The highest number of Dengue cases was recorded in 2023, affecting over 80 countries with over 6.5 million cases and more than 7,300 dengue-related deaths reported. While Dengue cases occur worldwide, the recent acceleration is especially prevalent in Latin America and Asia, exposing an increasing amount of the world's population. Early warning systems at regional and micro scales are critical for emergency managers and decision makers to facilitate data-driven distribution of resources and disease intervention strategies to minimize the impact on the population. Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to improve our ability to predict outbreaks and manage and prioritize public health interventions. Furthermore, remote sensing and other geospatial data hold immense potential in monitoring environmental conditions conducive for dengue incidence. Temperature, rainfall, humidity, elevation, and land use/ land cover all play major roles in the mosquito population dynamics that influence dengue transmission. Geospatial analytics and ML modeling can combine these environmental data with population characteristics and dengue monitoring to provide predictive assistance to local disease managers. Our team has developed the Disease Incidence and Resource Estimator (DIRE), which provides decision-makers a geospatial predictive tool for imminent dengue epidemics, as well as a recommendation of health resources required to control and treat diseases. We employ free and open-source geospatial tools to ingest and analyze the environmental, climatological and demographic conditions conducive to dengue development, construct ML models to predict the next month of dengue cases at a variety of administrative levels, and serve emergency managers with a web application to map and analyze dengue predictions and resource needs. Funded by the Wellcome Trust, this tool is a combined effort between The University of California San Diego, New Light Technologies, and UNICEF, and leverages an ensemble ML approach developed by the European Space Agency (ESA) and UNICEF. Employing open-source geospatial, modeling, and visualization tools has been critical as we construct this into a stable long-term solution for the public and nonprofit sectors. Our pipeline leverages urllib, BeautifulSoup, and cdsapi to acquire environmental data from NASA and ESA, and employs geopandas, rasterio, and rasterstats to analyze these data at each administrative level. The tensorflow, sklearn, and catboost libraries power a ML model for dengue incidence that includes CNN, LSTM, CatBoost, and SVM elements in a RandomForest ensemble. The web application is built using React, MapLibre, deck.gl, and Turf.js to allow emergency managers to interrogate both the spatial and temporal patterns in emerging dengue outbreaks. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/QA83HS/ Lake Anne Garrett Tate PUBLISH GG8T9H@@talks.staging.osgeo.org -GG8T9H Open-Access High-Resolution data for a Livable Planet en en 20251104T163000 20251104T170000 0.03000 Open-Access High-Resolution data for a Livable Planet The world is experiencing an unprecedented biodiversity crisis, with nearly one million species at risk of extinction and the rate of loss is accelerating. Conservation efforts are often hindered by limited access to high-quality, location-specific data on ecosystems and species distributions. We demonstrate how high-resolution, open-access data can support sustainable development and biodiversity conservation. Our paper leverages advances in machine-based pattern recognition to estimate species occurrence maps using georeferenced open data from the Global Biodiversity Information Facility (GBIF). We developed occurrence maps for around 600,000 species—including vertebrates, arthropods, mollusks, vascular plants, fungi, and others—using GBIF data. Species ranges were estimated using the “alphahull” algorithm, allowing for flexible, data-driven boundary mapping. We validated our results by comparing them with expert-derived maps from recent literature on mammals, ants, and vascular plants, finding a close similarity in global distribution patterns. We also generated extinction risk indicators based on threat and protection factors, leveraging open high-resolution satellite data. These maps reveal previously unrecognized patterns of biodiversity and extinction risk, particularly among underrepresented species groups. Our findings demonstrate the power of open-access environmental data combined with modern analytical tools to generate actionable insights for biodiversity monitoring. The resulting framework is scalable, replicable, and adaptable to future updates from evolving datasets emphasizing the critical importance of public access to high-resolution environmental data for addressing global challenges. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/GG8T9H/ Lake Anne Brian Blankespoor PUBLISH FDSKCH@@talks.staging.osgeo.org -FDSKCH The Democratization of Databases en en 20251105T090000 20251105T093000 0.03000 The Democratization of Databases Slides are at https://momjian.us/main/presentations/open_source.html#democratization PUBLIC CONFIRMED Keynote https://talks.staging.osgeo.org/foss4g-na-2025/talk/FDSKCH/ Grand Ballroom (Keynotes) Bruce Momjian PUBLISH K8CLFQ@@talks.staging.osgeo.org -K8CLFQ Keynote: To Be Determined en en 20251105T163000 20251105T170000 0.03000 Keynote: To Be Determined Place holder PUBLIC CONFIRMED Keynote https://talks.staging.osgeo.org/foss4g-na-2025/talk/K8CLFQ/ Grand Ballroom (Keynotes) Paul Ramsey PUBLISH 9WXLVQ@@talks.staging.osgeo.org -9WXLVQ MapLibre projects, in one status update en en 20251105T103000 20251105T110000 0.03000 MapLibre projects, in one status update This talk will cover all aspects of MapLibre efforts - the open source non-profit delivering the ubiquitous map rendering engine plus all tooling to convert data into interactive maps. The engine is used by organizations of every size, from tiny one person sites to Meta, AWS, and Microsoft as their primary map rendering engine. Come learn of the products we are developing, the new features we are excited about, the challenges and success, and the collaboration with the FOSS community and companies of all sizes. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/9WXLVQ/ Regency Ballroom B Yuri Astrakhan PUBLISH 3KFQ3B@@talks.staging.osgeo.org -3KFQ3B Revolutionizing Elections with GIS en en 20251105T110000 20251105T113000 0.03000 Revolutionizing Elections with GIS KNOWiNK is at the forefront of a new era in elections management, harnessing the power of GIS to bring unmatched precision and reliability to the electoral process. In elections, the stakes are high and the questions are critical: - Are you getting the ballot you're supposed to have? - Do your elections officials have the tools to check? - Are your pollworkers heading to the places where they're most valuable? Utilizing geospatial technologies, we can address these questions and enable accurate elections like never before. - Eliminate misassigned voters by using true geospatially defined legal boundaries for precinct and district assignment - Enhance transparency and auditability through geospatial reporting and visualization - Modernize election worker management and logistics with location intelligence   Traditional methods of precinct assignment and voter management often rely on outdated street ranges, leading to errors and inefficiencies that can undermine public trust, or worse yet, go unnoticed entirely. One of our latest innovations, GEOKiT, seamlessly integrates advanced GIS capabilities with comprehensive voter management applications, empowering election officials to manage their operations with spatial accuracy never before possible. GEOKiT leverages open-source technologies such as PostgreSQL, PostGIS, and OpenLayers to deliver a robust geospatial web components library that is rapidly re-usable across products with little to no code difference. Join us to learn how KNOWiNK is revolutionizing elections with GIS, and how this technology is shaping the future of democracy. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/3KFQ3B/ Regency Ballroom B John Ross PUBLISH XXWSQU@@talks.staging.osgeo.org -XXWSQU Building Open Source Teams en en 20251105T130000 20251105T133000 0.03000 Building Open Source Teams Slides are at https://momjian.us/main/presentations/open_source.html#teams PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XXWSQU/ Regency Ballroom B Bruce Momjian PUBLISH VASQBE@@talks.staging.osgeo.org -VASQBE The Power of Community and Collaboration in Open-Source Innovation en en 20251105T133000 20251105T140000 0.03000 The Power of Community and Collaboration in Open-Source Innovation This presentation will address the symbiotic relationship between Open-Source projects and the commercial organizations that rely on them, exploring how businesses can contribute to and benefit from their involvement in open-source communities. Unity in Code is not just a celebration of what has been achieved through open-source collaboration, but also a call to action for future contributions. It's an invitation to developers, technologists, industry leaders, and enthusiasts to join focuses, share their expertise, and contribute to the open-source movement for the greater good of the technology community and beyond. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VASQBE/ Regency Ballroom B Timothy Steward PUBLISH XJC8ZK@@talks.staging.osgeo.org -XJC8ZK Taking a proprietary process and move it to FOSS4G en en 20251105T140000 20251105T143000 0.03000 Taking a proprietary process and move it to FOSS4G Basically this talk will go over the good and the ugly with moving an ESRI centric project into a FOSS4G one and how opening the tools up has been better than keeping it a proprietary process. With any luck I'll try to do a demo of the tools. I should have move functionality by November as this has grow from 1 client to now 4. The weird side effect of this process is watching groups that traditionally went "We don't want a GIS" suddenly are building one with QGIS being the center point to this. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XJC8ZK/ Regency Ballroom B Randal Hale PUBLISH FFPHT7@@talks.staging.osgeo.org -FFPHT7 Geospatial Open Source and Selling the Contemporary Enterprise Software Experience en en 20251105T143000 20251105T150000 0.03000 Geospatial Open Source and Selling the Contemporary Enterprise Software Experience As an enterprise vendor of network management software to the Telco and Utility sectors, IQGeo makes heavy use of FOSS4G projects such as PostGIS and OpenLayers as well as lower-profile libraries such as Shapely. In 2025, how we talk about the value proposition of geospatial open source is deeply intertwined with adjacent concerns such as interoperability, security, scalability, and the end-user experience. Brian will discuss both the challenges and opportunities for FOSS projects in an enterprise environment that has moved beyond a simple “Open vs Closed” binary mindset to reckoning with paradigm shifts such as the Cloud and now, of course, AI. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/FFPHT7/ Regency Ballroom B Brian Timoney PUBLISH WGNU8V@@talks.staging.osgeo.org -WGNU8V Opening Up Urban Form and Development Trends en en 20251105T153000 20251105T160000 0.03000 Opening Up Urban Form and Development Trends The future of urban change belongs to everyone. Using free data available world-wide and writing a free and open-source tool, the Center for Geospatial Solutions at the Lincoln Institute of Land Policy (CGS) is opening an avenue to fill a gap in global urban planning research. Our tool presents a novel methodology that leverages readily available open datasets to create an objective, reproducible, and predictive understanding of urban development patterns. Our approach combines building footprints, transportation networks, and terrain data from OpenStreetMap and Overture Maps with innovative analytical techniques adapted from ecological science. By treating buildings as species and urban areas as habitats, we apply presence-only observation modeling techniques (MAXENT) to understand and predict urban development patterns. The resulting analysis allows cities to better anticipate and prepare for future growth patterns and can be regularly updated as new data becomes available. The free and open-source nature of the data, software used, and the tool’s codebase ensures consistency and accessibility across different urban contexts while maintaining methodological rigor. Proprietary software is expensive and not accessible to planners, community members, and government workers in locales that cannot afford to pay the license fee. Planners and other folks who rely on analyses based on public urban data are lacking an abundance of free open-source alternatives that build on the basics that software like ArcGIS and QGIS easily introduce. CGS is familiar with this problem: after presenting the ArcPy notebook at the World Urban Forum in Cairo in 2024, focus groups noted the potential barrier of use in countries where formal planning is not abundant. In cities and countries where formal planning systems are not part of the government, local or otherwise, both data and tools can be hard to find. The entire pipeline from development to user needs to be free and open-source to create new secondary datasets for research-supported policies. Esri products are generally well documented but hide key parameters in their tools that run complicated processes, choosing to make it easier for the user by estimating the best parameter based on the data. Translating from ArcPy to open-source Python packages required CGS to understand the analyses we were running more completely. The learning curve can be steep and while many examples and tutorials exist, they are not always suited to the user’s needs. Given that our intended audience for the tool is people who can use a desktop GIS software but are not experts in GIS, we set defaults for parameters that are challenging to understand, for example the bandwidth for a Kernel Density Estimate in sklearn. Because our code is open source, our parameters are documented and public, and more advanced users can modify the code easily and suggest changes that more adequately meet their needs. A second challenge was that not every Esri tool has a one-to-one match with an open-source alternative. Some processes take multiple packages or need to use a different solution to a similar problem. For example, to solve a simple routing problem of many points to one destination, the options through Esri are OD Matrix and Closest Facility. Both Esri options require several layers of set up to use and while OD Matrix is optimized for speed, it's not quick when the number of origins and/or destinations are large. However, using OSMNx, MOMEPY, and NetworkX to download and create the network dataset, select the node closest to the city center, and run an optimized shortest path length algorithm takes a fraction of the time that using Network Analyst does. The function we used from NetworkX is optimized for a 1:M problem, unlike the more generic N:M solver that Esri’s OD Matrix provides. Even though we needed to use multiple Python open-source packages to replace Esri’s Network Analyst, the resulting code maximizes speed and code readability, improving the user experience at multiple skill levels. Another benefit to using open-source software is that we can use data storage systems that are optimized for speed and compression. Proprietary products like ArcGIS rely on datatypes that do not always play well with open source software. Esri products also function inefficiently with some newer file formats that open source software packages can read natively. Esri products require a multi-file connection to use the column-oriented, compressed, and now natively geospatial, parquet file type. Parquet is our preferred vector file type due to its querying speed, compression, partitioning options, and the ease and speed of I/O with open-source tools, like Geopandas and DuckDB. Our tool uses parquet by default but can write outputs in shapefile and geopackage formats, allowing the user to choose the output that they are most comfortable using and enhancing accessibility. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/WGNU8V/ Regency Ballroom B Margo Atkinson PUBLISH 99DJJJ@@talks.staging.osgeo.org -99DJJJ Taming Dependency Hell: Effortless Dev Environments with Pixi en en 20251105T160000 20251105T163000 0.03000 Taming Dependency Hell: Effortless Dev Environments with Pixi Building applications that rely on specific GDAL versions or GPU-enabled ML libraries across platforms can quickly spiral into dependency hell. If you've wrestled with Conda environments, brittle Docker builds, or sluggish install times, you're not alone. This session introduces Pixi, a modern tool that makes dependency management simple and reliable. We’ll walk through real-world examples that show how to combine PyPI and Conda packages, manage multiple environments, define reproducible tasks, and build clean Docker images, all from a single config file. Whether you're publishing a library or deploying an application, Pixi helps you develop with confidence. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/99DJJJ/ Regency Ballroom B Thomas Maschler PUBLISH NSQTWZ@@talks.staging.osgeo.org -NSQTWZ GeoServer 3: motivation and and progress report en en 20251105T103000 20251105T110000 0.03000 GeoServer 3: motivation and and progress report This presentation provides an in-depth status update on GeoServer 3, the ambitious overhaul of the widely used open-source server for spatial data and web services. Announced as part of a community-driven crowdfunding effort, GeoServer 3 seeks to modernize the platform’s foundation to ensure it meets the growing demands of the geospatial community. We’ll first analyze the GeoServer 2.x status quo, and the effect of cascading changes that a “simple” Spring upgrade caused, turning the activity into a cross project overhaul, and how the large effort required got socialized and eventually brought to implementation via in-kind volunteering and a crowdfunding campaign driven by Camp2Camp, GeoCat and GeoSolutions. We will explore the planned milestones in the transition to GeoServer 3. These include critical refactorings, such as replacing aging libraries, adopting modern Java frameworks, and integrating support for the latest versions of GeoTools and GeoWebCache. Key technical advancements include the evolution and integration of ImageN for improved raster data processing, the migration from Wicket 7 to Wicket 10 for a modernized and more secure web user interface, and the adoption of Jakarta EE and Spring 6 to support enhanced security, scalability, and long-term compatibility with modern Java ecosystems. Join us to celebrate the progress, reflect on the lessons learned, and get inspired by what’s possible with GeoServer 3—a project that continues to empower geospatial professionals and organizations worldwide. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/NSQTWZ/ Reston ABC Andrea Aime Simone Giannecchini PUBLISH YDQNQK@@talks.staging.osgeo.org -YDQNQK State of GeoServer en en 20251105T110000 20251105T113000 0.03000 State of GeoServer GeoServer is a web service for publishing your geospatial data using industry standards for vector, raster and mapping, as well as to process data, either in batch or on the fly. GeoServer powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage, disseminate and analyze data at scale. This presentation provides an update on our community as well as reviews of the new and noteworthy features for the latest releases. In particular, we will showcase all the new features landed in the latest GeoServers. Attend this talk for a cheerful update on what is happening with this popular OSGeo project, whether you are an expert user, a developer, or simply curious what GeoServer can do for you. This presentation provides an update on our community as well as reviews of the new and noteworthy features for the latest releases. Attend this talk for a cheerful update on what is happening with this popular OSGeo project, whether you are an expert user, a developer, or simply curious what GeoServer can do for you. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YDQNQK/ Reston ABC Andrea Aime Simone Giannecchini PUBLISH TTR7XK@@talks.staging.osgeo.org -TTR7XK State of MapStore en en 20251105T113000 20251105T120000 0.03000 State of MapStore MapStore is an open source product developed for creating, saving and sharing in a simple and intuitive way maps, dashboards, charts and geostories directly online in your browser. MapStore is cross-browser and mobile ready, it allows users to: - Search and load geospatial content served using widely used protocols (WMS, WFS, WMTS, TMS, CSW, 3D Tiles) and formats (GML, Shapefile, GeoJSON, KML/KMZ etc..) - Manage maps (create, modify, share, delete, search), charts, dashboard and stories directly online - Manage users, groups and their permissions over the various resources MapStore can manage - Edit data online via WFS-T with advanced filtering capabilities - Deeply customize the look&feel to follow strict corporate guidelines - Manage different application contexts through an advanced wizard to have customized WebGIS MapStore viewers for different use cases (custom plugins set, map and theme) You can use MapStore as a product to deploy simple geoportals by using the standard functionalities it provides but you can also use MapStore as a framework to develop sophisticated WebGIS portals by reusing and extending its core building blocks. MapStore is built on top of React and Redux and its core does not explicitly depend on any mapping engine but it can support both OpenLayers, Leaflet and Cesium; additional mapping engines could be also supported (for example MapLibre GL) to avoid any tight dependency on a single engine. The presentation will give the audience an extensive overview of the MapStore functionalities for the creation of mapping portals, covering both previous work as well work for the future releases. Eventually, a range of MapStore case studies will be presented to demonstrate what our clients (like City of Genova, City of Florence, Halliburton, Austrocontrol and more) and partners are achieving with it. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/TTR7XK/ Reston ABC Tobia Di Pisa Stefano Bovio PUBLISH 7HRUAX@@talks.staging.osgeo.org -7HRUAX Building digital urban models for MapStore and Cesium en en 20251105T130000 20251105T133000 0.03000 Building digital urban models for MapStore and Cesium The presentation describes processes and open-source tools employed by the author and his team to build and consume digital models for urban environments. The results of these processes will be rendered in MapStore as 3D Tiles layers, an OGC community standard designed for streaming and rendering massive 3D geospatial content. MapStore WebGIS framework support for 3D Tiles and glTF models through the Cesium mapping library has been greatly enhanced to support a more powerful integration. The latest versions of MapStore also include improvements and tools for exploring 3D data such as Map Views, Styling, 3D Measurements, Annotations and more. Attendees will be presented with an overview of our work related to 3D data processing and visualization, and a selected city will be used to exemplify the processes. At the end of the presentation, attendees will be able to use the presented processes, tools and workflows to replicate them in different urban scenarios, finally visualizing them with the 3D tools of the MapStore WebGIS application. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/7HRUAX/ Reston ABC Tobia Di Pisa Stefano Bovio PUBLISH R9ZNTP@@talks.staging.osgeo.org -R9ZNTP Resampling Without Regrets: The Nonary Tree for EO Grids en en 20251105T133000 20251105T140000 0.03000 Resampling Without Regrets: The Nonary Tree for EO Grids Web Mercator and the quadtree have served web mapping well. They offer simplicity, consistency, and performance. But they fall short for Earth observation (EO) data. Their standard resolutions rarely align with common EO products like Landsat (30m), Sentinel (10m), or PlanetScope (3m). Worse, Web Mercator’s distortions can inflate global pixel counts by over 40%, leading to more storage and longer processing times. In this session, we introduce a novel approach: a grid system based on a nonary tree. It’s designed with EO in mind, aligning cleanly with typical spatial resolutions and enabling seamless resampling between them. Combined with a cylindrical equal-area projection, this system offers an efficient, resolution-friendly framework for storing, analyzing, and visualizing global EO datasets without the usual trade-offs. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/R9ZNTP/ Reston ABC Thomas Maschler PUBLISH 8MNKAG@@talks.staging.osgeo.org -8MNKAG All Latitudes, Longitudes, and Heights will be Changing en en 20251105T140000 20251105T143000 0.03000 All Latitudes, Longitudes, and Heights will be Changing For the first time in nearly 40 years, the foundation for all geospatial data in the U.S. is being overhauled to better support our geospatially enabled society and emerging technology such as GeoAI, digital twins, and digital project delivery. NOAA’s National Geodetic Survey (NGS) defines, maintains, and provides access to the National Spatial Reference System (NSRS), which serves as the basis for civilian surveying and mapping in the United States. Currently, NGS is in the process of modernizing the NSRS, updating the existing horizontal datums with a suite of geometric reference frames and the vertical datums with a gravimetric derived geopotential datum. The new system will provide a more accurate and consistent base, making all geospatial data more interoperable. The core elements of the new system were released for beta testing in June 2025. This session will discuss the limitations of the current system, benefits of the new system and the data and tools that will be made available. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/8MNKAG/ Reston ABC Galen Scott PUBLISH HWPTHZ@@talks.staging.osgeo.org -HWPTHZ Convolution PCA: Engineering independent intensity and texture features en en 20251105T143000 20251105T150000 0.03000 Convolution PCA: Engineering independent intensity and texture features Remotely sensed raster datasets of landscapes are commonly used as predictor variables (features) in many geospatial analyses. The information contained within these datasets span from cell values related to the earth’s surface (e.g., reflectance, temperature, and elevation) to patterns in cell values at coarser resolutions (texture) [1-5]. Both cell and texture values have been useful in sample design, classification, segmentation, and regression analyses [1-6] with a recent emphasis placed on texture and how texture can improve modeling results [1,3]. Within remote sensing, texture is quantified using convolution type analyses, which requires specifying weights that are multiplied by the cell values within a defined spatial window and summed together to attribute the center (focal) cell of that window [5]. While many common image filters (kernels) have been defined and are used to enhance, blur, and identify edges and patterns in surrounding cell values [5], the number of kernels that can be defined are infinite, making it difficult to know which kernels best quantify texture. Moreover, many cell and texture values are highly correlated and covary across bands and neighboring cells within a neighborhood window, potentially making those features redundant and less desirable for modeling. The classical approach to using texture in an analysis is to apply well known filters to input raster surfaces and use convolved outputs as features in a predictive model [7-9]. Alternatively, kernel weights can be optimally determined (learned) for a given task and applied to underlying remotely sensed data [3, 10, 11]. However, neither approach addresses covariance among band and neighboring cell values, which can have adverse effects on modeling and can substantially increase the total amount of processing. One common approach to address covariance for continuous data is to project data along shared axes of covariance and create independent features using a principal component analysis (PCA) [12]. For many remote sensing projects, PCAs have successfully been used to project multiband raster cell data onto orthogonal axes using component scores. Additionally, some of these same projects have successfully compressed the dimensionality of the data while keeping the majority of variation within the raster surface by selecting subsets of components that account for a known amount of variation in the data [13-16]. However, few remote sensing projects have addressed the issues of covariance in kernel cell values [9] and across image bands. Moreover, no studies have leveraged PCA component scores, derived from both band and neighboring cell values, to define kernel weights. In this study we evaluate the use of a PCA to project multispectral imagery along orthogonal axes derived from both band and neighboring cell values. Our procedure automates the selection of optimal kernel weights for multidimensional convolution kernels based on principal component scores and the proportion of the variation (information in the data) explained by each component. To evaluate the utility of these components for modeling, we compare model fit and complexity for models derived from our components and models derived from common band and texture transformations. The spatial extent of our study includes portions of the Custer Gallatin National Forest located in southeastern Montana, USA. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/HWPTHZ/ Reston ABC John Hogland PUBLISH VQRGMJ@@talks.staging.osgeo.org -VQRGMJ SpaceTimeIDs: Built for Boundaries, expandable to Digital Twins en en 20251105T153000 20251105T160000 0.03000 SpaceTimeIDs: Built for Boundaries, expandable to Digital Twins The use of Unique Identifiers (UUIDs) for managing spatial data has expanded in recent years, as have the development of geospatially-focused Digital Twins. This presentation explores the use case of SpaceTimeIDs, as developed for the management of the Large-Scale International Boundaries (LSIB) dataset, and extrapolates on the opportunities and technical gaps of expanding the concept beyond boundaries into other spatial feature types. The utility of SpaceTimeID approach is twofold: first, the ability to maintain and track change over time, while simultaneously maintaining connection to a digital document store associated with the dataset; and second, the ability to manage datasets at an individual component and aggregate dynamically into higher order semantic concepts (think individual boundary segments defined in various treaties vs the entire boundary line shared between two countries). Any industry in which spatial data evolves over time, needs to maintain links to relevant documents through time, and that manages data at multiple semantic and conceptual tiers can leverage the SpaceTimeID model. Currently, SpaceTimeIDs are fully deployed in the management of the LSIB, fully integrated into the Boundaries and Sovereignty Encyclopedia (BASE) web application, and publicly released as part of the official dataset in the U.S. National Spatial Data Infrastructure. Additionally, SpaceTimeIDs are used in the production and encoding of the World Polygons dataset. The current workflows leverage a full open source software stack that extends through the spatial database, desktop GIS, data science, and web application tiers. Core elements of the SpaceTimeID framework are reviewed and technical gaps related to broader adoption discussed. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VQRGMJ/ Reston ABC Joshua S Campbell PUBLISH YZ7AV7@@talks.staging.osgeo.org -YZ7AV7 VIPER Map Server; map data aggregation for NASA lunar missions en en 20251105T160000 20251105T163000 0.03000 VIPER Map Server; map data aggregation for NASA lunar missions NASA Volatiles Investigating Polar Exploration Rover (VIPER) is a planned Lunar mission conducted via a remotely commanded robot to prospect for water ice. Operational decisions for rover driving and instrument commanding will be compressed to minute-scale timeframes. VIPER requires scientists to make decisions like those delegated to Apollo astronauts, on the same time scales, remotely from Earth. The VIPER rover is 2.5m tall and 1.5m in length and width. Cameras are used for navigation, imaging and to generate point clouds used for terrain correction. To support prospecting, the rover carries a Neutron Spectrometer System (NSS), a Near-Infrared Volatiles Spectrometer System (NIRVSS), a Mass Spectrometer (MSOLO), and a drill (TRIDENT) which analyzes the drill cuttings. Many software packages and custom tools were developed and repurposed to support mission operations and were required to interoperate. Data must be aggregated and searchable via location and time. We developed the Map Server to store data from multiple inputs, to rebroadcast data and events, and to provide a flexible API for access. Data flows over multiple protocols. All data going to and from the rover will be sent over the Deep Space Network (DSN) using the CCSDS protocol. Protocols that the Map Server supports include: • Robotic telemetry from Rover Ground Software sent over ROS (open source Robot Operating System) • HTTP requests from various tools including o OpenMCT for time-controlled visualization in a web browser o MMGIS (built on Leaflet and Three.js) for map visualization within OpenMCT o VIPER Lunar route planning tool written in C# o Eclipse (Java) commanding tool for robot operators and engineers • REDIS to integrate with Manual Alignment image adjustment tools which uses OpenLayers • YAMCS to integrate with OpenMCT and receive CCSDS data Raster maps: During development, we were faced with mapping challenges. The ideal map projection is an offset polar stereographic lunar projection to support accurate metric measurements near the south pole of the Moon. Base GeoTIFF maps were created from imagery generated by the Lunar Reconnaissance Orbiter Camera (LROC), focusing on the area to be explored, and reprojected into our custom projection. We use LunaSERV (Arizona State University) as the backbone for storing and serving base maps and other raster map layers, since LunaSERV supports this Lunar map projection and the WMS protocol. We leverage GDAL to do GeoTIFF manipulation, both to correct and enhance base maps and to generate rasters on the fly. Data import and export: Map Server supports data export as WMS layers and GeoJSON. Members of the science team use external tools such as MATLAB, ArcGIS, JMP and Excel to do deeper analysis of the data. Implementation details: We developed Map Server as a set of Docker containers, to easily control configuration and deployment. Postgres with PostGIS is our database, extended to have our custom map projection for efficient queries and reprojection. The HTTP API was written in Python using Flask and SQLalchemy. We utilized libraries such as shapely and pyproj. Challenges: Some of the instrument data will be collected while the rover is driving, and it will be used by the science team to interpret what elements are in various areas. The rover’s plan might include repeated coverage of interesting areas. As the instrument data is received, it will be aggregated and run through complex filters to generate heat maps. Since the Map Server must provide these heat maps as layers for the various tools to use for visualization, we started with an approach of creating GeoTIFF raster layers. However, since the rover revisits overlapping regions or corrects its location, we often have to re-aggregate and recompute heat maps, which proved too compute intensive to deliver for real time analysis. To solve this issue, we turned to PostGIS. By storing the raw data with timestamp and location in the database, we can configure MMGIS/Leaflet to automatically pass the visual map bounds and zoom level to Map Server’s API, which uses PostGIS queries to aggregate the data on the fly and generate GeoJSON to display in MMGIS. With this approach, the data can be dynamically controlled and delivered on time. Lessons learned: A complex mission with many interconnected systems using different protocols absolutely relies on centralized services to unify data and translate between various protocols. Each software system is typically developed focusing on its own needs and requirements, which may include Earth-based WGS84 latitude and longitude projections, local coordinate systems from a defined origin, or another projection entirely. By carefully defining our own projection and properly installing it in systems that support map projections (PostGIS, LunaSERV) we can leverage the capabilities of those systems to support the varying needs of the client software. VIPER’s Map Server could not have been developed on time and in budget without free and open source software. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YZ7AV7/ Reston ABC Tamar Cohen David Lees PUBLISH 3P9GCZ@@talks.staging.osgeo.org -3P9GCZ Scalable Architecture for Distributed Spatiotemporal Analytics en en 20251105T103000 20251105T110000 0.03000 Scalable Architecture for Distributed Spatiotemporal Analytics This proposal outlines a novel architecture designed for processing large-scale GPS, ADS-B, and AIS data. The platform supports analytical use cases involving millions of vehicle data points streaming in from diverse global sources. At its core, our ingestion pipeline transforms raw positional pings into logical chunks of sequential data referred to as "trips." A trip typically represents a complete real-world journey such as a vehicle commute from home to office or a commercial flight from NYC to SFO. A common analytical workflow involves a user defining one or more polygons on a map along with time windows of interest. The objective is to identify trips that either start, end, or pass through these spatial regions during the specified intervals. These queries are submitted to the analytics backend, which returns the corresponding set of trips that satisfy the user's criteria. Our initial implementation was built atop the PostgreSQL ecosystem, leveraging PostGIS and stored procedures to handle complex geospatial queries. This solution was effective driving initial customer engagement. It scaled to approximately 200TB across several PostgreSQL instances split with data divided by contractual clients. However, with rapid customer onboarding and exponential data growth, we encountered scalability limitations. Chief among these were: - Skewed load distribution, leading to persistent disk I/O bottlenecks on certain customer instances. - Limited observability and debuggability of stored procedures, making it difficult to estimate execution time or troubleshoot performance regressions. These constraints prompted a broader exploration of distributed data processing frameworks and storage engines. Following months of prototyping and benchmarking, we transitioned to a stack composed entirely of open-source technologies, selected for their scalability, community support, and interoperability. While PostgreSQL continues to serve essential functions such as maintaining metadata, user-submitted geometries, and analytics job results the heavy-lifting is now delegated to a distributed analytics backend comprising: - Apache Spark: Serves as the compute layer for the analytics. Spark jobs are a direct replacement for the SQL functions, offering more flexibility, observability and parallelism. - Apache Cassandra: Serves as the primary indexing layer for fast, pre-filtered trip lookup. - Apache Iceberg: Used to store high-fidelity trip data on cost-effective object storage, indexed for efficient retrieval. Cassandra hosts three core indexing tables aligned with common user query patterns: - trip_start: Indexes trips by their starting geohash and timestamp - trip_end: Indexes trips by their end geohash and timestamp - trip_passthrough: Indexes trips by intermediate geohashes and timestamps Each table is partitioned using a compound key consisting of a 6-character geohash and a 1-hour epoch bin, yielding partitions approximately 1x1 km² in area and 60 minutes in duration. This granularity ensures that partitions stay within Cassandra’s optimal size threshold (~100MB), enabling low-latency reads. When users submit polygon-based queries, the application decomposes the polygons into a set of intersecting geohash cells. These are used to scan the corresponding partitions in Cassandra. A second, more precise geometric filtering stage follows, eliminating false positives inherent in geohash approximations. This two-step approach minimizes expensive spatial computations and improves overall throughput. Once candidate trips are identified, their full-resolution ping data is retrieved from Iceberg. By deferring access to this large, immutable dataset until late in the query lifecycle, we drastically reduce the volume of data read, improving performance. Spark then assembles the final result set in the desired format and persists it back into PostgreSQL for downstream consumption or display. The platform currently ingests approximately 400GB of new data per day and manages an active historical archive exceeding 500TB. It is designed to operate at global scale, ingesting and serving spatiotemporal data across continents with efficient query latency and fault-tolerant architecture. Our talk will focus on the lessons learned migrating from relational database to a distributed architecture and high level walk through of the tradeoffs involved when choosing a distributed technical stack at scale for geospatial analytics. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/3P9GCZ/ Lake Audubon Prashant Swarnapuri PUBLISH G9JDX7@@talks.staging.osgeo.org -G9JDX7 Geospatial Mission Operations with the Multi-Mission Geographic Information System en en 20251105T110000 20251105T113000 0.03000 Geospatial Mission Operations with the Multi-Mission Geographic Information System Geospatial techniques and software provide a shared mission spatial context for science and engineering decision making. The “where” is necessary knowledge for science planning, both in terms of the aircraft or spacecraft location in time, the instrument pointing, and capturing new science data. Mapping shines when integrated early during mission formulation and then again in the surface mission development cycle. In a mapping context, collaboration is critical for large, globally distributed science and engineering teams, to enhance mission planning, analysis, and data fusion, to increase situational awareness to save time and effort, as well as maximize science return. At NASA/JPL, we assemble formal mapping teams, Mapping Specialists or Geospatial Analysts, who provide new maps and mapping data daily based on spacecraft position, targeted science, and strategic planning. We’ve built an open source, web-based, geospatial application for direct use during operations. The Multi-Mission Geographic Information System (MMGIS), created though the NASA Advanced Multi-Mission Operations System (AMMOS), was designed to support science operations for the Earth and other planets like Mars. MMGIS takes advantage of open data format standards and Free and Open Source Geospatial (FOSS4G) spatial/mapping libraries. MMGIS has allowed multiple missions to progress beyond strictly COTS desktop packages or closed source programs to a web-based, open-source geospatial application that allows both engineering and science teams more rapid access to gigabytes or even terabytes of data and without having to download large files or learn more complicated software. Multiple NASA centers (JPL, Johnson Space Center (JSC), AMES), foreign space programs (European Space Agency (ESA)), and external collaborators (Scripps Institute of Oceanography) have embraced MMGIS for their mission operations on Mars (Mars Science Laboratory (MSL)/Curiosity rover, Mars2020 Perseverance rover, Mars Helicopter/Ingenuity, pre-mission planning work on Mars Sample Return, InSight geophysical lander), future robotic (e.g. CLPS Lunar VIPER rover exploring the lunar South Pole) and initial testing at the SP Crater, Arizona analogue site for human extravehicular (EVA) exploration (ARTEMIS) on the Moon, and on Earth for the Earth Mineral Dust Source Investigation (EMIT) onboard the International Space Station (ISS) for sharing data location and Methane detections, the Multi-Angle Imager for Aerosols (MAIA) showing 2.5 micron particle detections from globally distributed surface sampling locations, and the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program with time-series maps allowing the query of millimeter-scale displacements at thousands of geodetic stations. Over the past year, we’ve expanded into supporting airborne field campaigns, both on the ground for missions like BioSCape which flew multiple hyperspectral and lidar instruments in the south African Greater Cape Floristic Region (GCFR) and in the aircraft for the FireSense campaign, mapping controlled burns in Alabama and Georgia, where real-time aircraft position and georeferenced image updates were provided in real-time to the flight crew and downlinked to ground crews for analysis. We’ve also supported field campaigns at analogue sites in California, Arizona, and Iceland. MMGIS input is in the form of open formats like GeoJSON, GeoTIFFs, Cloud-Optimized GeoTIFFs, Tile Map Service (TMS) tiled rasters, and WMS/WMTS protocols. We also support enhancements to the GeoJSON format to include properties per vertice, which allows creating ‘hotline’ linear features or properties (e.g. slope per vertice), encoding 32-bit datasets into a PNG RGBA TMS format for on-the-fly mesh generation, time-based raster data display leveraging the TMS tile format, time-based TMS tile mosaicking, advanced editing like unioning features, and recent additions of TiTiler COG visualization with mosaicing and SpatioTemporal Asset Catalogs (STAC) support. The latter two new capabilities expand use of COGs and existing STAC instances to better support large, current and future Earth-based missions (e.g. Surface, Biology, and Geology, SBG). Vector formats can be stored separately as GeoJSON files or within our internal Postgres/POSTGIS database which allows indexing, spatiotemporal selection, and vector tile support. The goal is to provide the “quicklook” geospatial products for initial data exploration in a geospatial context to allow later selection and processing for higher order research analyses. The software also supports webhooks and websockets to allow interaction with other data servers and provide real-time updates. Being open source and updating one repository (no “clone and own”), software updates are rapid and deployable to other missions, saving development costs and implementing new features easily. We have three types of APO endpoints: ‘Configure’, ‘JavaScript’, and ‘Utility’. The Configuration API supports adding data and creating, modifying, mission layers. These updates can be pushed via websockets. The ‘JavaScript’ API is designed for embedding within another application. Such a deployment allows transmitting data and changes back and forth between two applications to either keep them in sync (time or data wise) as well as allowing one to ‘drive’ the other. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/G9JDX7/ Lake Audubon Dr. Fred J. Calef III Paul Ramirez PUBLISH P7BZKY@@talks.staging.osgeo.org -P7BZKY Space2Stats - exploring meso-scale geospatial data en en 20251105T113000 20251105T120000 0.03000 Space2Stats - exploring meso-scale geospatial data Often in development, we look to national policies and comparisons between them to find examples of effective policy, whether this is in economic growth, job creation, or public service delivery. However, this focus on national comparisons limits our perspective to one scale of analysis. Within most countries, there is both more than one level of governance, and therefore policy and public investment, but there are also uneven initial endowments and resources, levels of private investment and capital flows, and other unique factors of place. Exploring these differences at various geographic scales can shed light on why development policy can be more effective in one region than another. For reasons of comparability, cost-effectiveness, and the mandates of data collectors, this spatially disaggregated data is often not available from one country to the next, and often not comparable. The open-source database Space2Stats provides the foundation for these consistent, global analytics. We will introduce the database, explore its use cases, and describe how we are pushing the envelope in Cloud Native Geospatial (CNG) solutions to make the data accessible to technical and non-technical users. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/P7BZKY/ Lake Audubon Benjamin Stewart PUBLISH KDMLWW@@talks.staging.osgeo.org -KDMLWW Geospatial Technology Radar: A Report Against a Turbulent 2025 Backdrop en en 20251105T130000 20251105T133000 0.03000 Geospatial Technology Radar: A Report Against a Turbulent 2025 Backdrop For the past two years, our team has unveiled a Geospatial Tech Radar (2023 version, 2024 version) at FOSS4G-NA with the goal of highlighting industry trends, technologies, and solutions, while functioning as a knowledge sharing platform for the geospatial community. The latest radar grapples with innovations that have greatly impacted the geospatial industry this year in the face of funding uncertainty for many projects as well as the continued proliferation of AI and Machine Learning. We will discuss new updates to the radar and spotlight blips that have dramatically improved or shifted since they were first introduced in previous editions of the radar. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/KDMLWW/ Lake Audubon Lauren Frederick PUBLISH EYJRWH@@talks.staging.osgeo.org -EYJRWH Hand Drawn Maps en en 20251105T133000 20251105T143000 1.00000 Hand Drawn Maps Drawing maps by hand has a long history. Hand-drawn maps have applications as art, in designing board games, illustrating manuscripts, and helping plan professional geospatial projects, just to name a few. Today, we often rely on technology to create maps, but there are a number of reasons to work with good ol’ pencil and paper: Understand an area in greater depth Explore cartographic decisions before working with digital tools Assess knowledge of an area, either by yourself or with a group Create art It’s fun! In this workshop, participants will develop skills and workflows for creating maps drawn by hand on paper. Along the way, we will also explore cartographic (map design) principles and open data sources that are applicable to maps and workflows using any tool, analog or digital (i.e. you’re gonna learn stuff you can apply to your “real job”). PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/EYJRWH/ Lake Audubon Michele Tobias PUBLISH PENWEV@@talks.staging.osgeo.org -PENWEV Publishing Maritime AIS Big Data via GeoServer, Databricks, and Azure en en 20251105T143000 20251105T150000 0.03000 Publishing Maritime AIS Big Data via GeoServer, Databricks, and Azure The amount of data we have to process and publish keeps growing every day, fortunately, the infrastructure, technologies, and methodologies to handle such streams of data keep improving and maturing. GeoServer is an Open Source web service for publishing your geospatial data using industry standards for vector, raster, and mapping. It powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage and disseminate data at scale. We integrated GeoServer with some well-known big data technologies like Kafka and Databricks, and deployed the systems in Azure cloud, to handle use cases that required near-realtime displaying of the latest AIS received data on a map as well background batch processing of historical Maritime AIS data. This presentation will describe the architecture put in place, and the challenges that GeoSolutions had to overcome to publish big data through GeoServer OGC services (WMS, WFS, and WPS), finding the correct balance that maximized ingestion performance and visualization performance. We had to integrate with a streaming processing platform that took care of most of the processing and storing of the data in an Azure data lake that allows GeoServer to efficiently query for the latest available features, respecting all the authorization policies that were put in place. A few custom GeoServer extensions were implemented to handle the authorization complexity, the advanced styling needs, and big data integration needs. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PENWEV/ Lake Audubon Nuno Oliveira Simone Giannecchini PUBLISH NUA3EM@@talks.staging.osgeo.org -NUA3EM Serving earth observation data with GeoServer: addressing real world requirements en en 20251105T153000 20251105T160000 0.03000 Serving earth observation data with GeoServer: addressing real world requirements Never before have we had such a rich collection of satellite imagery available to both companies and the general public. Between missions such as Landsat 8 and Sentinels and the explosion of cubesats, as well as the free availability of worldwide data from the European Copernicus program and from Drones, a veritable flood of data is made available for everyday usage. Managing, locating and displaying such a large volume of satellite images can be challenging. Join this presentation to learn how GeoServer can help with with that job, with real world examples, including: - Indexing and locating images using The OpenSearch for EO and STAC protocols - Managing large volumes of satellite images, in an efficient and cost effective way, using Cloud Optimized GeoTIFFs. - Visualize mosaics of images, creating composite with the right set of views (filtering), in the desired stacking order (color on top, most recent on top, less cloudy on top, your choice) - Perform both small and large extractions of imagery using the WCS and WPS protocols - Generate and view time based animations of the above mosaics, in a period of interest - Perform band algebra operations using Jiffle Attend this talk to get a good update on the latest GeoServer capabilities in the Earth Observation field. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/NUA3EM/ Lake Audubon Simone Giannecchini PUBLISH 3T93YX@@talks.staging.osgeo.org -3T93YX Developing Methodologies to Union Overlying Building Footprint Datasets en en 20251105T160000 20251105T163000 0.03000 Developing Methodologies to Union Overlying Building Footprint Datasets As increasing numbers of regional and global scale building footprint datasets become available, users are presented with new challenges in source selection. The existing and computationally significant techniques required to morphologically match buildings between diverse methods in building detection becomes daunting when attempting to apply this methodology on the scale of hundreds of millions of buildings in an efficient manner. Rethinking this challenge, we propose a new methodological approach utilizing a global scale grid, from one-to-one or one-to-many challenges to one using statistical methods to compare various building sets over small areas. This new approach allows for choices between various sources based on user defined selection criteria with significantly reduced computational requirements and substantially accelerated decision making. Harnessing the common 30 arc second grid resolution found through numerous products, we propose a decision-making methodology at the grid cell level. This flexible methodology, utilizing statistical metrics of the objects in the cell can be applied to a variety of input data sources that are relevant to building modeling and presents the opportunity for comparisons on a common grid between input data sources with widely varying resolutions. With this framework, we can now leverage statistical comparisons to easily choose between footprint sources using global standards such the Global Human Settlement Layer (GHSL) or population models as a comparison point, make decisions about which building feature source is ideal for the specific problem at hand, or choose between different building height estimates. In this presentation, we will discuss the process of deriving this workflow, how we believe it can be applied, and some applied examples. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/3T93YX/ Lake Audubon Jason Kaufman PUBLISH TVK8AL@@talks.staging.osgeo.org -TVK8AL GeoNode: Use Cases & Custom Applications en en 20251105T103000 20251105T110000 0.03000 GeoNode: Use Cases & Custom Applications Node is a Web Spatial Content Management System based entirely on Open Source tools whose purpose is to promote the sharing of data and their management in a simple environment where even non-expert users of GIS technologies can view, edit, manage, and share spatial data, maps, prints and documents attached. GeoNode is an open source project initiated in 2010 by the World Bank Global Facility for Disaster Reduction and Recovery group (GFDRR), but from 2011 is entirely run by the developer community that the project has been able to attract. It claims some large organizations among its contributors such as the United Nations, the World Bank and the European Commission as well as many NGOs and private companies. Supported by a vast, diverse and global open source community, GeoNode is an official project of the Open Source Geospatial Foundation (OSGeo). Using an open source stack based on mature and robust frameworks and software like Django, MapStore, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its own SDI or geospatial portal. GeoNode provides a large number of user-friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. Over the years, GeoSolutions has been involved in a number of projects, ranging from local administrations to global institutions, involving GeoNode deployments, customizations and enhancements. A gallery of projects and use cases will showcase the versatility and effectiveness of GeoNode, both as a standalone application and as a service component, for building secured geodata catalogs and web mapping services, dashboards and geostories. In particular the recent advancements in data ingestion and harvesting workflows will be presented, along with the many ways to expose its secured services to third party clients. Examples of GeoNode’s builtin capabilities for extending and customizing its frontend application will be showcased. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/TVK8AL/ Lake Thoreau Giovanni Allegri PUBLISH QFK3WV@@talks.staging.osgeo.org -QFK3WV State of GeoNode en en 20251105T110000 20251105T113000 0.03000 State of GeoNode GeoNode is a Web Spatial Content Management System based entirely on Open Source tools whose purpose is to promote the sharing of data and their management in a simple environment where even non-expert users of GIS technologies can view, edit, manage, and share spatial data, maps, prints and documents attached. GeoNode is an open source project initiated in 2010 by the World Bank Global Facility for Disaster Reduction and Recovery group (GFDRR), but from 2011 is entirely run by the developer community that the project has been able to attract. It claims some large organizations among its contributors such as the United Nations, the World Bank and the European Commission as well as many NGOs and private companies. Supported by a vast, diverse and global open source community, GeoNode is an official project of the Open Source Geospatial Foundation (OSGeo). Using an open source stack based on mature and robust frameworks and software like Django, MapStore, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its own SDI or geospatial portal. GeoNode provides a large number of user-friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. The purpose of this presentation is to introduce the attendees to those which are the GeoNode current capabilities and to some practical use cases of particular interest in order to also highlight the possibility of customization and integration. Finally, we will provide a summary of new features added to GeoNode in the last release up to the latest releases of GeoNode together with a glimpse of what we have planned for next year and beyond, straight from the core developers. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/QFK3WV/ Lake Thoreau Giovanni Allegri PUBLISH ACNDXK@@talks.staging.osgeo.org -ACNDXK Mapping Democracy: Visualizing the Voice of a Million Americans en en 20251105T113000 20251105T120000 0.03000 Mapping Democracy: Visualizing the Voice of a Million Americans How can data visualizations represent the complex mosaic of public opinion? Headlines focus on the big polling numbers, but what about the real numbers on the ground? How do opinions vary from neighborhood to neighborhood and county to county? True Views (trueviews.org) is a mapping platform that answers these questions in a beautiful and engaging way. Using complex statistical models, political scientists at Harvard Law developed a fine-grained analysis of Americans' beliefs across 32 different policy areas. With years of data and over one million responses to analyze, a ZIP code level model was developed, allowing the public and policymakers to visualize how opinions vary across the map. To enable the exploration of this massive dataset, we (greeninfo.org) built a lightweight mapping application that is optimized for performance and accessibility. The vector tile data format (PMTiles) was crucial for allowing users to rapidly explore data of this size. These tiles allow the storage of both geographic and tabular data, so the browser only has to download a few small files for the map to render. Each shape contained the numbers for each policy question, allowing the cartography to change instantly based on user input. To enable this speedy design, we used a robust data pipeline (bash, Python) to pre-process and upload these datasets to the cloud, avoiding the need for slow databases and APIs. But it’s not just about speed: from day one, we built TrueViews to be fully keyboard navigable, ensuring that people of all abilities can explore the data for themselves. Where possible, WCAG best practices were followed, and data was always accessible in multiple ways—both in a table and on the map. As we developed the application, we explored the data and discovered surprising common ground on topics portrayed as divisive in news headlines. If we uncovered value and meaning while developing the tool, we knew that this tool could be a powerful catalyst for change. In this talk, I will share our process from start to finish, starting with design exploration, data pipeline development and tools, and implementation tips for speed and accessibility. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/ACNDXK/ Lake Thoreau Stephen Smith PUBLISH HZM7GE@@talks.staging.osgeo.org -HZM7GE PostGIS Feature Frenzy en en 20251105T130000 20251105T133000 0.03000 PostGIS Feature Frenzy There are hundreds of PostGIS functions, and thousands of PostGIS users, and this talk will attempt to summarize them all in under 30 minutes (impossible!). Key functions, architectures, deployment options, use cases, users, developers, dependencies, this talk could cover 4 hours but will improbably end in just 30 minutes. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/HZM7GE/ Lake Thoreau Paul Ramsey PUBLISH QXHYEN@@talks.staging.osgeo.org -QXHYEN Enterprise-Grade Open Source: Operational Insights from CoreSpatial Deployments en en 20251105T133000 20251105T140000 0.03000 Enterprise-Grade Open Source: Operational Insights from CoreSpatial Deployments As federal and defense organizations increasingly adopt open-source geospatial technologies, the need for robust, secure, and enterprise-ready solutions has never been greater. This presentation shares real-world lessons from supporting CoreSpatial—a modular geospatial platform built entirely on open standards and open-source components—in mission-critical environments. Drawing on operational deployments across DoD and civilian agencies, we'll explore strategies for achieving scalability, security, and long-term maintainability with software like GeoServer, GDAL, PostGIS, OpenLayers, Cesium, and MapStore2. Attendees will gain actionable insights on avoiding common pitfalls, establishing sustainable support models, and tailoring open-source stacks for high-stakes federal use cases. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/QXHYEN/ Lake Thoreau Jason Newmoyer PUBLISH FUDXRK@@talks.staging.osgeo.org -FUDXRK Making Postgres Central in Your Data Center en en 20251105T140000 20251105T143000 0.03000 Making Postgres Central in Your Data Center Slides are at https://momjian.us/main/presentations/extended.html#central PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/FUDXRK/ Lake Thoreau Bruce Momjian PUBLISH 8HPNZV@@talks.staging.osgeo.org -8HPNZV Mapping the Future in 3D: Youth, Data, and Open Platforms en en 20251105T143000 20251105T150000 0.03000 Mapping the Future in 3D: Youth, Data, and Open Platforms YouthMappers is an international network of university-based student chapters that create and use open geospatial data to address real-world development challenges. With over 400 chapters in more than 70 countries, YouthMappers empowers young people to become leaders in open mapping while advancing goals in sustainability, resilience, and humanitarian action. This presentation will showcase how this community is using open-source tools to build both data and capacity, while supporting a global movement of youth who map for impact. A major focus of this presentation will be the YouthMappers Academy, which is relaunching in August 2025 as a fully open-source and publicly accessible e-learning platform. Formerly limited to internal consortium members, the new Academy features self-paced learning modules that cover essential open geospatial technologies, such as OpenStreetMap (OSM), JOSM, Field Papers, KoboCollect, and more. You can find the full list of modules here: https://www.youthmappers.org/academy. Participants will learn how the Academy fosters skill-building through a digital badging system that certifies proficiency in various aspects of open mapping—from beginner techniques to advanced workflows. Key modules in the Academy include: - Introduction to OpenStreetMap - Editing with JOSM and ID Editor - Field Survey Development (Survey Instruments & Tools) - Visualizing and validating data - Specialized workflows including 3D tagging and visualization By making this resource open and freely available to anyone, the Academy extends YouthMappers’ reach far beyond universities, offering tools and training to anyone interested in open geospatial knowledge. In the second part of the talk, I will highlight a new series of tutorial videos that guide users through 3D mapping with open tools, a rapidly evolving area of interest for both students and professionals. These videos provide step-by-step instructions for: 1) Mapping in 3D: Remote Mapping 3D Features with Mapillary & JOSM Learn how to remotely identify 3D building features using imagery and Mapillary, apply height and shape tags, and contribute them to OpenStreetMap using JOSM. This advanced tutorial is ideal for experienced mappers looking to enhance the depth and realism of their edits. 2) Mapping in 3D: Bringing Geospatial Data to Life with Cesium Stories Discover how to use Cesium ion—a web-based platform—to render and interact with 3D Tiles derived from OSM data. This tutorial demonstrates how mappers and developers can transform raw 3D information into immersive, interactive geospatial scenes using just a browser. 3) Mapping in 3D: A Step-by-Step Guide to Using FieldPapers for 3D Mapping in OSM This beginner-friendly tutorial teaches how to plan fieldwork using printable Field Papers atlases, annotate building dimensions and roof types on paper, and digitize the data back into OSM. This hybrid approach empowers mappers in low-connectivity settings to contribute high-value, detailed spatial data. These tutorials not only support the technical development of open mappers, but also reflect YouthMappers’ core philosophy: learn by doing, lead by mapping. Whether you're a student, humanitarian mapper, or open-source advocate, these resources help build practical skills while contributing to a global data commons. In the final portion of the session, I will explore the broader impact of these initiatives–how YouthMappers chapters use mapping projects to address local development needs, partner with NGOs, and build leadership pathways in their communities. From disaster resilience mapping in the Philippines, to urban infrastructure mapping in Latin America, to health access projects in Africa, the YouthMappers network is shaping the next generation of geospatial changemakers. Attendees will leave the session with: - A practical understanding of the YouthMappers Academy platform and how to access its free training materials - Inspiration from real-world youth-led mapping projects - Resources to learn or teach 3D mapping using open platform - Insights into building community through open mapping and open data This talk is ideal for: - Educators and program managers seeking open geospatial curriculum - Students and youth interested in global collaboration and mapping - OSM community members exploring advanced workflows like 3D mapping - Open-source advocates looking to expand participation and equity in the geospatial world PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/8HPNZV/ Lake Thoreau Elodie Nix PUBLISH P7LLYA@@talks.staging.osgeo.org -P7LLYA Grounded: Local Datum Mapping with DroneDeploy en en 20251105T153000 20251105T160000 0.03000 Grounded: Local Datum Mapping with DroneDeploy Building on the DroneDeploy's ITRF2014 approach, presented at last year's FOSS4G NA, this session will serve as a deep dive into how DroneDeploy has utilized PROJ and other open-source GIS software to collect, and keep data in local datums. In this session, we'll cover how we're switching over to use local datums for collecting RTK and PPK corrected images, and some of the challenges along the way. Our co-presenter, an NGS veteran, will share practical insights, deployment challenges, and best practices for high-accuracy drone mapping and surveying. Attendees will learn about useful approaches for handling data in local datums, improving spatial accuracy, and minimizing transformation complexity. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/P7LLYA/ Lake Thoreau Ben Scholer Ryan Hippenstiel PUBLISH 9LBWPD@@talks.staging.osgeo.org -9LBWPD Advancing the Python Geospatial Stack: Building Community Across Domains en en 20251105T160000 20251105T163000 0.03000 Advancing the Python Geospatial Stack: Building Community Across Domains Proposal Summary: The Python geospatial ecosystem is experiencing rapid growth and accelerating adoption across numerous disciplines. From large-scale Earth system analysis with Pangeo, to the rise of spatial data science in the social and life sciences powered by PySAL and GeoPandas, and the development of scalable, interactive environments via GeoJupyter, Python is becoming the platform of choice for geospatial research and applications. This Birds of a Feather (BoF) session will bring together developers, users, and stakeholders to foster dialogue across projects and domains. By strengthening connections within the community, we aim to identify shared needs, reduce fragmentation, and collaboratively advance the capabilities and sustainability of the Python geospatial stack. Rationale and Motivation: Python’s geospatial stack is a vibrant yet decentralized ecosystem built on modular, interoperable libraries. Key components include: Pangeo, enabling cloud-native, parallel analysis of massive geoscience datasets; GeoPandas, Shapely, and Fiona, offering intuitive, high-level tools for working with vector data; PySAL, a comprehensive library for spatial data science, supporting spatial econometrics, clustering, accessibility modeling, and more; xarray, rasterio, and rio-tiler for multi-dimensional and raster data handling; Cartopy, Datashader, and Bokeh for advanced geospatial visualization; GeoJupyter, integrating these tools within scalable, Jupyter-based platforms for reproducible research and education. Despite this progress, challenges remain—ranging from fragmented documentation and inconsistent APIs to a lack of coordinated governance and limited visibility across domains. Session Goals: Facilitate connections between developers and domain experts in Earth, social, and life sciences. Share success stories (e.g., GeoJupyter deployments, Pangeo use cases, PySAL in education). Identify interoperability needs and opportunities for tighter integration across libraries. Discuss education, outreach, and documentation strategies to support new users. Explore community governance and sustainable funding models. Initiate collaborative efforts to close gaps and develop shared roadmaps. Target Audience: Developers of Python geospatial and spatial data science libraries Applied researchers and analysts in academic, public, and private sectors Educators, data scientists, and students interested in spatial methods Open-source contributors and infrastructure providers Anyone curious about the evolving landscape of geospatial Python Format and Structure: The session will begin with brief overviews by representatives from key projects (e.g., PySAL, GeoJupyter, Pangeo), followed by thematic breakout discussions (e.g., vector/raster integration, cloud-native tools, education). We will close with a group synthesis of key takeaways and actionable next steps. Conclusion: This BoF session is a unique opportunity to unify a diverse yet overlapping community of practice. By sharing insights, aligning efforts, and cultivating relationships, we can strengthen the foundation of Python’s geospatial ecosystem and accelerate the next generation of spatial data science. PUBLIC CONFIRMED Birds of a Feather https://talks.staging.osgeo.org/foss4g-na-2025/talk/9LBWPD/ Lake Thoreau Taylor M. Oshan Sergio Rey PUBLISH GUL3RN@@talks.staging.osgeo.org -GUL3RN Teaching FOSS4G: Sharing Local Data for Improved Community Decision Making en en 20251105T103000 20251105T110000 0.03000 Teaching FOSS4G: Sharing Local Data for Improved Community Decision Making Recognizing the need for improved access to data vital for addressing vexing community challenges, Iowa State University Extension and Outreach’s Community and Economic Development (CED) unit developed the Community Indicators Program in 2013 and the Data Science for Public Good (DSPG) Young Scholars summer outreach program (https://dspg.iastate.edu/) in 2020. These programs have been successful at producing and sharing demographic, economic and other state data through the curation of meaningful and timely data and informative publications and dashboards. The programs have also worked with communities to utilize the Community Learning through Data Driven Discovery (CLD3) https://cld3.org/our-approach/ framework to address local issues and provide local stakeholders with the ability to make data-supported decisions. While these efforts have been a good start to improve issue awareness and decision-making in smaller and rural communities, many of the indicators that are informative measures in larger communities are not readily available or are at a granularity that is not suitable for local decision-making. Additionally, access to software and the skills to operate this software can be a barrier to working with this data once it is identified. To address this issue, a program focused on Science Education and Workforce Development was piloted in 2024. This program prioritized 1) increasing local capacity for data literacy 2) increasing technical skills to access data 3) increasing ability to share local data. The program developers recognized the need for utilizing Free and Open-Source software to limit the cost and access barriers to visualizing and sharing local data with the community. Software skills are developed though hands-on-training using local data examples. The current suite of free and open-source software is accessible and scalable to meet the needs of many small communities. The software included in the program consists of QGIS, GeoJSON.io, Google Sheets, GitHub, and Tableau Public. Used together, this software allows participants to easily make their local community data publicly available at little to no cost. Examples of some of the data projects include: the number of recurring community events, use frequency of park shelter rentals, number of volunteer organizations, number of youth/adult parks and recreation participants, attendance rates at council/supervisor meetings, number of permits for home improvements, library check outs, or even the number and spatial location of trees planted by the city. During this presentation, the presenter will share several of these examples demonstrating how this software can be used by those new to data science or geospatial technology. Additionally, the presentation will include some techniques that can be used to increase the utility and delivery of spatial data visualizations created with these tools. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/GUL3RN/ Lake Fairfax Christopher J. Seeger Bailey Hanson PUBLISH YXKWLL@@talks.staging.osgeo.org -YXKWLL OpenAccess for OpenSource: Writing and Publishing for the FOSS4GNA Community en en 20251105T110000 20251105T113000 0.03000 OpenAccess for OpenSource: Writing and Publishing for the FOSS4GNA Community This year at FOSS4GNA, the Academic Committee is excited to partner with Stacks, an open-access, peer-reviewed academic journal, to publish a special issue highlighting work from our community. In this talk, we’ll cover the fundamentals of Open Access publishing—what it is, why it matters, and how it aligns with the open-source ethos that defines FOSS4GNA. Whether you’re an academic, a practitioner, or somewhere in between, this session will demystify the publishing process and show how you can share your work more widely. You’ll be introduced to Stacks Journal and its short-form “2-pager” article format, which is especially suited to the kinds of technical insights, tools, and applied projects that FOSS4GNA attendees bring to the table. We’ll also touch on best practices for writing for an academic audience, and how to publish your data alongside your work to increase its reach and reusability. If you’ve ever considered turning a workshop, tool, or project into a publication, but weren’t sure how, this talk is for you. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YXKWLL/ Lake Fairfax Jessica Breen PUBLISH KJT8BW@@talks.staging.osgeo.org -KJT8BW Reproducing geographic analysis studies as open science project-based learning en en 20251105T113000 20251105T120000 0.03000 Reproducing geographic analysis studies as open science project-based learning Open geographic information science research practices can be applied to reproduce prior studies as project-based learning opportunities for undergraduate students to learn new methods and improve upon prior research. We report on the structure and results of an advanced geographic methods seminar in which we reproduced studies of geographic patterns of crime in Connecticut using geographically-weighted regression (Meng 2021), redlining and present-day residential water infrastructure in United States urban areas using binary logistic regression (Sterling et al. 2023), and heat exposure at carceral facility locations in the contiguous United States using spatial joins and linear regressions (Tuholske et al. 2024). The open science framework is designed to improve research transparency in order to improve research quality, access, and efficiency. For geographic research, we use a standardized research compendium template with version control to organize project metadata, data, procedures, code in computational notebooks using open-source software, preregistered analysis plans, and post-analysis living reports. We document analysis plans with the research design prior to analyzing data in order to control sources of researcher bias and improve understanding of the research problem. We publish and update living reports afterward to make results and changes to the research design accessible and transparent. For open geographic information science studies, it should be possible for researchers and students to conduct reproduction studies in which they repeat the same procedures with the same data and confirm the findings with the same results. In the research seminar, we learned open science workflows with the R and GitHub platforms in four scaffolded stages. First, we learned basics of Git version control and Markdown language by creating simple Jekyll websites on GitHub. Second, we practiced using the template research compendium and open science workflows for a GIS lab on gerrymandering. Third, we repeated a demonstration reproduction study of COVID-19 and disability using the same template. Finally, we undertook a reproduction study of their own for the second half of the seminar. Three teams of two students each completed reproduction studies through four project stages. First, we searched literature for reproduction study candidates and selected a single study aligned with their thematic and methodological interests to reproduce. Second, we closely read the study and its supplementary materials and researched its data sources. We initialized a research compendium and completed its project metadata, data source metadata, and analysis plan in an Rmarkdown computational notebook, all prior to analyzing data. Third, we added R code blocks to the analysis plan notebook to implement the study and produce resulting statistics, tables, and figures. While implementing the study, we documented “unplanned deviations” whenever we had to adjust research design decisions due to ambiguities or inconsistencies with the original study or data. Finally, we rendered and published analysis reports in our GitHub repository webpages and peer-reviewed the legibility, accuracy, completeness, and functionality of our research compendia. Each reproduction study presented substantial challenges and learning opportunities, varying in availability of data, code, and methodological details. Meng and Sterling et al. described data sources and research design in their articles, but did not provide data or code. Tuholske et al wrote insufficiently about data and methods in their short article narrative, but provided supplementary materials with additional details and a GitHub repository with data and code. In our attempts to reproduce the studies, we encountered challenges with large data volumes, use of old data or code libraries, and ambiguities in methodological details and organization of supplementary materials. We identified uncertainties and threats to validity rooted in boundary effects, scale effects, construction of indicators, selection bias, and treatment of data with zeros, geometry errors, or missing data. Our findings have direct implications for improving both reproducibility and the quality of research design and reporting. Overall, we reproduced substantive portions of each study by writing and modifying spatial R code. We created public reproducible research compendiums for each reproduction study, and critically reviewed the study research designs for important sources of uncertainty and geographic threats to validity. Pedagogically, the reproduction studies were opportunities to apply project-based learning to authentic challenges contributing to open science in the geographic information science community. 1. Meng 2021, DOI:10.5719/hgeo.2021.152.5 https://github.com/opengisci/RPr-Meng-2021 2. Sterling et al 2023, DOI:10.1038/s41893-024-01293-y https://github.com/opengisci/RPr-Sterling-2023 3. Tuholske et al. 2024, DOI:10.1038/s41893-024-01293-y https://github.com/opengisci/Rpr-Tuholske-2024 PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/KJT8BW/ Lake Fairfax Joseph Holler Samuel Barnard Matthew Mills PUBLISH 99ULNX@@talks.staging.osgeo.org -99ULNX Growing GRASS en en 20251105T130000 20251105T133000 0.03000 Growing GRASS Join us for a lively overview of the current state of the GRASS project, where community meets cutting-edge geospatial technology. Whether you're a longtime power user or a newcomer curious about GRASS, this talk will highlight the major strides the project has made in the past year – from revitalized governance and community growth to technical breakthroughs – and offer a glimpse into what's next. During the talk, we will address how GRASS has strengthened its governance and support structure by bringing in new members to bolster sustainable leadership and new fiscal sponsorship with NumFOCUS. We will also review GRASS community-building initiatives, such as the NSF-backed efforts that allowed GRASS to establish a mentoring program for new contributors, support our Student Grant program, and hold the GRASS Developer Summit 2025 in Raleigh, NC. We will highlight this past summer's Google Summer of Code project, which demonstrates how community mentoring feeds innovation. The talk will also address GRASS's new logo and branding initiative over the past year, aiming to give the project a modern look while keeping its iconic elements. Notably, "GRASS GIS" is now officially just GRASS – a simpler name that the community has used colloquially for years. To celebrate, the team launched an online swag shop with GRASS-themed apparel, stickers, and more. We will also look at recent strides in community outreach and learning resources, such as a new tutorial website and the modernization of GRASS's documentation platform. On the development side, we will show off what the GRASS development team has been hard at work delivering in terms of new features, improved performance, and better integration as part of GRASS 8.5. Under the hood, the team made significant code quality and security improvements, fixing issues flagged by automated linters and code scanners. These efforts pave the way for stricter continuous integration checks and a more robust codebase. The build system is also being modernized: GRASS is transitioning to CMake for easier compilation and maintenance, and an official Conda package is on the way, simplifying installation for Python/R data scientists and lowering entry barriers. As we celebrate these achievements, we're also looking ahead. The GRASS roadmap outlines ambitious goals for the next few years. We plan to maintain annual releases (GRASS 8.6 is already on the horizon for 2026) and continue improving distribution and integration – think one-click installs via Conda, tighter bridges to QGIS and R, and refined Python and R APIs for smooth scripting. Sustainability remains a core focus: the project actively pursues new grants, sponsors, and community donations to ensure long-term development while spreading infrastructure knowledge and lowering maintenance overhead to avoid burnout. In short, the state of GRASS is strong and dynamic. This talk will offer an informative yet exciting tour of the project's recent milestones across community and technology. We invite everyone – from newbies to veteran developers – to see how far GRASS has come and to get inspired about where it's heading. Learn about the latest capabilities, meet the people behind the project, and discover how you can be part of the next chapter of GRASS! PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/99ULNX/ Lake Fairfax Corey White PUBLISH J3G7MJ@@talks.staging.osgeo.org -J3G7MJ GRASS Meets Longest Flow Paths, Shortest Compute Times en en 20251105T133000 20251105T140000 0.03000 GRASS Meets Longest Flow Paths, Shortest Compute Times We present r.lfp, a new GRASS addon for computing the Longest Flow Path (LFP) across a large number of watersheds, built for scalable high-performance hydrologic modeling. Based on the Memory-Efficient Longest Flow Path (MELFP) algorithm, r.lfp delivers substantial performance improvements by combining tail recursion with hybrid OpenMP parallelism and minimal memory usage, making it especially well suited for large digital elevation models (DEMs). r.lfp integrates seamlessly into the GRASS ecosystem, leveraging GRASS's native data structures and parallel processing framework. It supports both subwatershed-level and watershed-level LFP analysis modes, and offers reproducible results even across varied multi-threaded environments. The module's design emphasizes computational efficiency, reproducibility, and scriptability, enabling rapid analysis in research and operational workflows. In benchmark comparisons, r.lfp achieves orders-of-magnitude speedups over traditional implementations, without sacrificing accuracy. It also plays a key role in supporting advanced hydrologic modeling tasks, such as river network analysis and travel time estimation. As a contribution to the open-source geospatial community, r.lfp demonstrates how modern algorithmic design and memory-conscious parallelism can revitalize established GIS platforms like GRASS. Its development is part of a broader initiative to modernize and extend GRASS for continental-scale hydrologic modeling, with strong emphasis on scalability, interoperability, and scientific transparency. We highlight case studies on continental datasets to showcase r.lfp's performance, scalability, and integration potential with other GRASS modules and external tools. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/J3G7MJ/ Lake Fairfax Huidae Cho PUBLISH Q7CJCG@@talks.staging.osgeo.org -Q7CJCG Estimating trail bridge impacts on rural populations with Python en en 20251105T140000 20251105T143000 0.03000 Estimating trail bridge impacts on rural populations with Python Eighty percent of people coping with extreme poverty reside in rural areas, where basic transportation access is often lacking. This limits their ability to reach critical destinations such as hospitals, schools, and markets. Although governments and the development sector recognize the importance of rural infrastructure, data inequity remains a critical barrier, with even the most fundamental data needed to identify and address transport barriers frequently unavailable. This lack of data presents a major roadblock to scaled planning and investment. Bridges to Prosperity (B2P) is a nonprofit organization that partners with governments and remote communities to create access to essential health care, education, and economic opportunities by facilitating the construction of trail bridges and other rural transportation infrastructure. A key aspect of understanding the impact of this work is accurately estimating the number of people affected by new infrastructure. These estimates drive investment in the rural access sector and inform stakeholders where infrastructure is needed. However, B2P faces challenges in quantifying impacted populations due to the rural, data-scarce environments it works in and the lack of fixed catchment areas for trail bridges. B2P’s ongoing efforts to measure impacted populations include developing a randomized control trial (RCT), conducting pre-construction needs assessments in nearby villages, extracting estimates from the WorldPop gridded population dataset, and placing cameras to capture volume and direction of traffic across bridges. This presentation introduces B2P’s open geospatial programming methodology for estimating population impact at scale. Using open-source Python GIS packages such as GeoPandas and Rasterio and piloting GeoJupyter in JupyterLab, B2P compared its community-level needs assessment data with the 2022 Rwandan Census and global gridded population datasets, including WorldPop, GPW, GRUMP, LandScan, and GHS-POP. These comparisons have enabled B2P to confidently generate population estimates based on catchment areas defined in collaboration with the RCT research team. These estimates also feed into more complex impact models built from open data which paint a picture of where rural transportation solutions are most needed and how communities stand to benefit from trail bridges in the future. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/Q7CJCG/ Lake Fairfax Adele Birkenes PUBLISH CLZQTB@@talks.staging.osgeo.org -CLZQTB Rising Waters, 3D Worlds: Cesium-OSM-Powered Flood Simulations en en 20251105T143000 20251105T150000 0.03000 Rising Waters, 3D Worlds: Cesium-OSM-Powered Flood Simulations This project presents a 3D flood simulation game built using OpenStreetMap data and the Cesium for Unity engine to help users visualize the impacts of climate change and sea level rise on their own communities. Designed with YouthMappers in mind, the simulation enables users to explore localized flood scenarios and fosters climate risk awareness through immersive, personalized experiences. Future developments include interactive character navigation by land, sea, and air to deepen user engagement. Supported by the Cesium Ecosystem Grant and YouthMappers, this initiative transforms abstract climate data into vivid, relatable visual narratives. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/CLZQTB/ Lake Fairfax Michael Mann PUBLISH LPKDRW@@talks.staging.osgeo.org -LPKDRW pyplaces-a Python Package for Retrieving Open Places Data en en 20251105T153000 20251105T153000 0.00000 pyplaces-a Python Package for Retrieving Open Places Data Poster will be a review of the need for the pyplaces package, scope of the package, common usage with examples, and future scope. Need: Datasets in the geoparquet format can often be hard to access for users(academics, planners) that aren't adapted to ingesting data using Apache Arrow or SQL(via DuckDB, etc). This package bridges that gap by providing a way to access these large datasets with one line of Python code with and address or bounding box. Scope: Geoparquet datasets that are freely available. Currently: Overture Maps, Foursquare Open Places, and OSM Layercake(soon). pyplaces aims to allow users to access prior versions of these datasets as well. Common usage: Each dataset has a suite of functions(get from bounding box, place or address), poster will contain common examples for a few of the datasets. Looking at the dataset schema is another common use. Advanced use for datasets with places: categories table for each places dataset can be searched via a find categories function that can be used to filter the places data. Basic examples mapped on folium(or other similar medium) will be included. Future scope: non implemented features that are up and coming will also be listed PUBLIC CONFIRMED Poster https://talks.staging.osgeo.org/foss4g-na-2025/talk/LPKDRW/ Lake Fairfax Ted Banken PUBLISH JVCWYK@@talks.staging.osgeo.org -JVCWYK Modeling loss with FOSS: Python workflow evolution en en 20251105T160000 20251105T163000 0.03000 Modeling loss with FOSS: Python workflow evolution This presentation describes and illustrates the transformation of workflows for hazard data aggregation and risk modelling from proprietary GIS software to FOSS. The work, which begins with collecting geospatial data and ends with analyzing the performance of a predictive model on a series of points, requires contributions from several cross-collaborative teams with different levels of access and familiarity with geospatial software. A preliminary process for the research and development of the predictive model relied heavily on ArcGIS software on local machines, and it quickly presented itself as a challenge that collaborators in the model development had neither access to nor expertise with the proprietary software. The result of the preliminary GIS work was typically in the form of static visualizations exported from ArcGIS Pro, accompanied by summary statistics as support. Efficiency via repeatability was the primary goal in redesigning this process, which could be addressed with either licensed solutions like ModelBuilder and ArcPy or free and open source software. We found that free and open source Python libraries provided similar functionality to the proprietary licensed Python solutions with the additional benefit of improved documentation and a wider user base online, which aided in troubleshooting errors. Most of the highly manual data collection process evolved into a few compact functions making use of the Requests library, working in conjunction with BeautifulSoup to capture URLs from a host website or API endpoint and import data directly into a Python workflow. Geopandas can be used to hold that data in the form of a temporary geodataframe, and provides functionality for transformations, joins, buffers, and other operations similar to what is found in ArcGIS Pro. Pandas–Geopandas’s sibling for tabular data analysis–also has a massive catalog of functions for generating summary statistics and aggregating data. These capabilities, along with the familiarity that our non-geospatial collaborators have with Pandas, makes it easy to share our workflows and to make tweaks to the process as a team. We also improved the visualization output component of this workflow in passing Geopandas geodataframes to Folium for the generation of live Leaflet maps for active exploration without licensed software. Having all of this housed within the common format of a Jupyter notebook makes it possible to send an entire workflow, from data scraping to visualization, to a collaborator without an Esri license. That collaborators can see on their own machine a) where the data is coming from, b) how it’s transformed, and c) how it looks spatially in any area they’d like to see is a significant evolution to a more efficient, transparent, and repeatable process. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/JVCWYK/ Lake Fairfax Dominic Daniels PUBLISH KACZJL@@talks.staging.osgeo.org -KACZJL Atlas: Postgres meets GeoAI at scale en en 20251105T103000 20251105T110000 0.03000 Atlas: Postgres meets GeoAI at scale Earth Index aims to make our entire planet searchable, transforming billions of satellite image chips into high-dimensional embeddings to unlock insights into environmental change. Storing and efficiently querying this global dataset presents a significant scalability challenge – particularly with the budget of a non-profit. We’ll show how we’ve created a fast, open source solution for less than 1% of the cost of a proprietary solution. This talk details our approach to geospatially sharding and partitioning the massive embedding dataset across a distributed Postgres cluster. We’ll start by looking at design challenges including efficient data distribution, schema design, gridding schemes, global identifiers and query optimization. We’ll then cover the practical performance achieved – including query latency for nearest neighbor searches, data ingestion rates, and index build times at scale. Crucially, we will present a transparent analysis of the cost implications, comparing our self-managed, open-source Postgres architecture against the projected costs of specialized managed vector database services, demonstrating significant cost efficiencies. This should be a fascinating talk for anyone interested in massive datasets at the intersection of geospatial, AI and open source. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/KACZJL/ Lake Anne Tom "Hutch" Ingold PUBLISH YQZZLG@@talks.staging.osgeo.org -YQZZLG Natural Language GeoServer Queries Using OpenAI and WPS en en 20251105T110000 20251105T113000 0.03000 Natural Language GeoServer Queries Using OpenAI and WPS As open geospatial tools become more powerful and widely adopted, there's an increasing need to make them more accessible—especially for non-technical users. One promising path toward accessibility is enabling natural language interaction with spatial data services. In this presentation, I will introduce a new GeoServer community module under development that integrates OpenAI’s GPT API with GeoServer's Web Processing Service (WPS) framework to allow users to pose plain-language questions—such as “Where are the parcels over 5 acres in Montgomery County?”—and have them translated automatically into valid WFS ECQL filters. The module uses GeoServer’s internal catalog and LayerInfo metadata to provide context for the OpenAI model, enabling it to interpret queries with knowledge of: Available layers and attributes Attribute types and spatial references Layer-specific semantics (e.g., field names like acreage, parcel_id, etc.) This approach turns GeoServer into a more human-friendly spatial server, letting analysts, planners, and decision-makers interact with complex spatial data services without writing ECQL, CQL, or understanding WFS schemas. Key topics covered in the presentation: Technical architecture of the integration: WPS process, OpenAI prompt design, catalog introspection Demo of real-world queries and outputs Use cases in planning, environmental analysis, and public engagement Next steps for community adoption and feedback Attendees will come away with: A concrete understanding of how to combine GeoServer with LLMs Inspiration for extending their own systems with conversational geospatial interfaces Information on how to contribute to or test the module as it matures PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/YQZZLG/ Lake Anne Joseph Miller PUBLISH VS9EJN@@talks.staging.osgeo.org -VS9EJN Simulated Experts, Real Insight: A MetaPanel on GeoAI en en 20251105T113000 20251105T120000 0.03000 Simulated Experts, Real Insight: A MetaPanel on GeoAI What if you could gather the world's leading voices in GeoAI—from open data champions and critical geographers to spatial ML pioneers and field responders—all in one room? What if they weren’t human, but still knew their stuff? In this thought-provoking session, we present the MetaPanel: a simulated roundtable of AI-generated expert personas crafted using large language models. Each represents a unique ideological and technical stance—from Maya Ríos, an Indigenous data sovereignty advocate, to Prof. Otto Reinhardt, a spatial ontologist with strong opinions about everything (especially your coordinate system). They’ll tackle topics at the heart of the FOSS4G community: open-source GeoAI tooling, ethical data use, AI-driven mapping for disaster relief, the risks of centralized models, and the future of collaborative spatial intelligence. This session offers a creative, engaging way to explore the current state of GeoAI and where it’s headed—through the lens of synthetic experts who reflect the complexity and diversity of thought shaping our field. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/VS9EJN/ Lake Anne Rich Fecher PUBLISH W3NTRH@@talks.staging.osgeo.org -W3NTRH RescueMap‑AI: Open Source Geo‑AI for Disaster Response en en 20251105T130000 20251105T133000 0.03000 RescueMap‑AI: Open Source Geo‑AI for Disaster Response In the first hours after a disaster, finding survivors trapped under rubble or cut off by floodwaters is the single most important factor in saving lives. Although new sensors and mapping tools exist, rescue teams still rely on proven methods such as trained dogs because the current technology often fails when infrastructure is damaged and time is scarce. RescueMap‑AI is a lightweight, scalable, open‑source system that combines geospatial data, crowdsourced reports, and machine‑learning‑based clustering to give responders real‑time, actionable maps. Many field teams still use manual GPS logging, static satellite images, or drone fly‑overs. These approaches need stable power, high‑bandwidth links, and specialist staff, resources that are usually absent in the chaotic environment that follows an earthquake or hurricane. Conventional AI platforms also assume cloud access and powerful GPUs. RescueMap‑AI is designed for laptops, tablets, and rugged field computers, and it works even when the network is offline. The platform has three main components. First, a simple input layer allows citizens, volunteers, and responders to submit reports through a mobile app, a browser form, or SMS in low‑bandwidth areas. Second, an integration layer merges these reports with freely available geospatial data, such as OpenStreetMap extracts, cached satellite tiles, or publicly released aerial imagery. Third, a processing layer applies a fast density‑based clustering algorithm that highlights areas where several distress signals overlap, creating an instant priority list for rescue deployment. RescueMap‑AI follows an offline‑first design. Teams start by caching the base map for their region. After that, the entire workflow, including clustering and visualization, runs locally. When connectivity returns, the system synchronizes new reports with a central server so that headquarters can see the evolving situation. This approach bridges the gap between disconnected field teams and command centers. During the talk, I will walk the audience through a simulated earthquake response in an urban neighborhood. Volunteers report sightings of trapped people through the app. RescueMap‑AI ingests these points, groups them by proximity, and displays “hot spots” on a web map that refreshes every few seconds. Field commanders can export the hot spots as GeoJSON or CSV to load into navigation units or print on paper. The presentation will also cover present limitations and the roadmap. Current work focuses on improving location accuracy when GPS is degraded, authenticating user reports to reduce noise, and integrating drone imagery for rapid updates of blocked roads. RescueMap‑AI is planned to be fully open source. The code base is written in Python and JavaScript with dependencies kept minimal. Contributions from the FOSS4G community are welcome, especially testing in varied terrains and translations of the user interface. By lowering the technical barrier, the project aims to extend advanced rescue mapping to volunteer groups, rural fire departments, and NGOs that cannot afford proprietary tools. Attendees will learn practical methods for fusing open geospatial data with compact machine‑learning models, tips for building resilient offline applications, and ideas for mobilizing local communities through crowdsourced mapping. The talk will also outline possible pathways for integrating open‑source rescue technology into national preparedness programs, an aspect that supports my broader research on how software can reduce disaster mortality. RescueMap‑AI shows that effective geo‑AI does not require expensive hardware or closed ecosystems. It only needs the right balance of open data, simple algorithms, and thoughtful design. I invite conference participants to explore the software, propose new modules, and collaborate on future deployments so that the next time disaster strikes, responders will reach victims faster. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/W3NTRH/ Lake Anne Mohammed Bilal Ahmed PUBLISH XFNCMP@@talks.staging.osgeo.org -XFNCMP Precision in Language and the Future of “Little Data” Analytics en en 20251105T133000 20251105T140000 0.03000 Precision in Language and the Future of “Little Data” Analytics The open-source geospatial community has built powerful tools that manage and analyze massive datasets with unprecedented speed and steadily decreasing cost. These tools now support analyses at continental and global scales and increasingly contribute to the training of AI models. Despite the focus on scale, we often overlook the foundational geographic data infrastructure that provides essential analytical context. This oversight can introduce ambiguity and hinder the interoperability of our data and services. For instance, what do we actually mean by a “state” or a “dam”? This talk highlights the essential role of data architecture, linked data, ontologies, and metadata in geospatial analysis. It will show how applying FAIR principles - making data findable, accessible, interoperable, and reusable - can improve quality, enhance human understanding, and enable machine-readiness. These elements form the foundation of effective, AI-driven analysis in the open-source geospatial ecosystem. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/XFNCMP/ Lake Anne Joel Schlagel PUBLISH AVRS7P@@talks.staging.osgeo.org -AVRS7P Geoconnex: Anchoring AI in Reality with the Internet of Water en en 20251105T140000 20251105T143000 0.03000 Geoconnex: Anchoring AI in Reality with the Internet of Water With the rapid growth of AI-powered large language models (LLMs), the internet is increasingly able to answer a broad range of questions. However, the reliability of these answers are often in doubt because they are not required to be truthful. Minor inaccuracies may be acceptable when drafting emails or summarizing articles, but they become far more consequential when the questions relate to public trust and safety. For AI systems to provide reliable, precise, and contextualized answers about the real world, they must be grounded in structured, authoritative data. Knowledge graphs offer a framework for linking diverse datasets across institutional and disciplinary boundaries, allowing AI models to retrieve more trustworthy answers. The geoconnex.us project supports the creation of an open, community-contributed knowledge graph linking hydrologic features in the United States as an implementation of the Internet of Water Principles, particularly that “modern data infrastructure increases the usefulness of water data and enables its broadest possible application.” It offers persistent, canonical identifiers of real-world water-related entities, enabling data publishers to take full advantage of linked data while empowering data consumers to discover and reuse it. The project also drives contribution to the growing open source ecosystem of tools to publish and consume geospatial information products. As an implementation of W3C Web Best Practices and the OGC Second Environmental Linked Features Interoperability Experiment, geoconnex.us extends standards like Google Structured Data Markup to support structured, semantic representations of rivers, lakes, watersheds, monitoring stations, and more. Web Pages that adopt this markup can be crawled and indexed into the public knowledge graph, thereby enabling authoritative, fact-driven answers. This session will walk participants through the open source technologies that make this possible, how to contribute data, and how to access and navigate the graph, as well as demonstrating real-world use cases. It will explore how LLMs can interact with the geoconnex.us system to surface reliable, structured information about water in the United States and enable data-informed decision-making in water management. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/AVRS7P/ Lake Anne Benjamin Webb Colton Loftus PUBLISH PYYKPV@@talks.staging.osgeo.org -PYYKPV What’s new in 3D Tiles en en 20251105T143000 20251105T150000 0.03000 What’s new in 3D Tiles 3D Tiles facilitates efficiently streaming and rendering massive, heterogeneous 3D geospatial data such as point clouds, buildings, and photogrammetry. The 3D Tiles specification is designed to be flexible and extensible, allowing it to be used in a wide variety of domains. This talk will focus on Bentley’s/Cesium's latest efforts to add or improve support for a wide variety of data types in 3D Tiles and glTF: Gaussian splats, voxels, AEC models, and time-dynamic data. Additionally, the talk will highlight a couple of projects in the broader 3D Tiles ecosystem. 3D Gaussian Splatting, or 3DGS, was introduced at SIGGRAPH 2023 and kicked off a wave of excitement as a novel way to produce high-fidelity 3D reconstructions. Cesium has been working with folks from Bentley, Niantic Spatial, and Esri to formalize an extension for 3DGS support in glTF, as well as a corresponding extension for 3DGS compression. Adding hierarchical level-of-detail (HLOD) for efficient streaming via 3D Tiles is an active area of research. Voxels are a common data format for many science and engineering disciplines. For example, oceanographers and climate scientists may model ocean temperatures using a voxel format. Cesium has developed an extension for 3D Tiles, and is working on a glTF extension, to add support for voxels in 3D Tiles. The voxel grid structure may be cubic, cylindrical, or spherical (the latter two are less common “in the wild”, but preferable for certain geospatial use-cases). Many use cases for 3D Tiles are focused on the earth, but we’ve begun looking to the stars! A 3D Tiles extension for arbitrary ellipsoids has been merged, and Cesium has even published a moon terrain dataset. Preliminary discussions, though in their infancy, have begun on a potential Mars dataset. Architecture, engineering and construction (AEC) models are becoming increasingly detailed and expansive, requiring new strategies for streaming and rendering. Cesium has created a Design Tiler for converting AEC models into 3D Tiles. IFC and OBJ files are currently supported, with ongoing efforts to add support for additional formats, such as Bentley iModel. Many enhancements (some AEC-specific, some that benefit AEC use cases but are more general) to 3D Tiles and glTF are also in the works, including improved metadata support and more flexible and efficient rendering options (see e.g. glTF extension proposals EXT_mesh_primitive_restart and EXT_mesh_primitive_edge_visibility). Adding support for time-dynamic data has long been on the horizon for 3D Tiles. Visualizing a massive infrastructure asset, for example, is interesting and compelling, but having the ability to visualize changes over time completely changes the game. Cesium has been working on an efficient tiling scheme for time dynamic data, which is built upon 3D Tiles 1.1 and handful of new extensions (see here). The approach minimizes redundant data by only encoding changes to the tileset at discrete timestamps. The 3D Tiles ecosystem continues to grow. The popular, cross-platform Godot game engine has added support for streaming and rendering 3D Tiles. Additionally, the web3d consortium has begun work to create a x3dom loader for 3D Tiles 1.1. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PYYKPV/ Lake Anne Jake Adelgren PUBLISH PLHHJC@@talks.staging.osgeo.org -PLHHJC From Metadata to Pixels: Loading STAC Data the Smart Way en en 20251105T153000 20251105T160000 0.03000 From Metadata to Pixels: Loading STAC Data the Smart Way STAC makes it easy to organize and search geospatial data using spatiotemporal queries, without worrying about file paths. But turning that metadata into usable data can still be a tedious process. Manually resolving file locations and opening individual files isn't scalable. Luckily, there’s a better way. In this session, we'll show how to use GDAL drivers like STACIT, STACTA, and GTI, as well as tools like ODC STAC, to transform STAC query results into working datasets. These tools allow you to access and lazily load large volumes of data without ever having to think about file paths. Whether you're building interactive applications or conducting large-scale analysis, you can now skip the plumbing and focus on the insights. PUBLIC CONFIRMED Talk https://talks.staging.osgeo.org/foss4g-na-2025/talk/PLHHJC/ Lake Anne Thomas Maschler