BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-2022//speaker//TDPHD3 BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2022-WSHGDN@talks.staging.osgeo.org DTSTART;TZID=CET:20220824T120000 DTEND;TZID=CET:20220824T123000 DESCRIPTION:In the past seven years\, the Brazilian Army Geographic Service has been putting effort into migrating the entire geospatial production c hain into open-source. The first step was the use of PostgreSQL + PostGIS as the primary data storage solution\, then the use of open standards such as OGC WMS and WFS for data sharing\, then the use of QGIS as the main so ftware for data acquisition\, and finally\, the development of DSGTools\, QGIS plugin with several tools for data quality control and cartographic f inishing.\n\nThe Geoinformation Production Management System (GPMS) comes as the latest addition to our open-source stack. It has two main goals: to manage the distribution of jobs between the staff automatically\; and sta ndardize workflows\, layers\, styles\, tools\, processes and parameters of each job. \n\nFor the first goal\, the manager can create a profile for e ach team member\, setting which parts of the workflow they are qualified t o execute. When the user asks for a job\, the system matches his qualifica tions with the available jobs on the current project and gives him the hig hest priority job. This\, combined with the visualization of the live prod uction status on QGIS\, helps the manager improve resource allocation's de cision management.\n\nFor the second goal\, the manager can configure in d etail each workflow\, setting which database the job will be executed on\, which layers the user needs to access\, which styles are available for ea ch layer\, which QGIS Processes the user should run with pre-set parameter s\, which resources the user has access to\, such as imagery and DEMs. GPM S also does the permission control in the PostgreSQL database based on the job the user currently is executing and allows spatial filtering\, so the user only can work in a spatially defined subset of the data. In this way \, when a user receives a job\, he has everything he needs to complete it. \n\nAll the jobs information\, such as user\, start timestamp\, end timest amp\, and job parameters\, are stored in the system\, allowing the automat ic generation of metadata compatible with Brazilian Standards and visualiz ations of the project's current state.\n\nAs the Brazilian Army Geographic Service has 5 Centers of Geoinformation\, the use of a production managem ent system helped with the standardization of procedures\, since a standar d configuration can be defined and replicated between centers to use the s ame consistent workflow\, with the same database schema\, same layers per job\, same QGIS Styles\, processes\, parameters and so on.\n\nThe GPMS is available on Github on https://github.com/1cgeo/sap as a Node web service and requires a QGIS plugin for the client (https://github.com/1cgeo/Ferram entas_Producao) and a QGIS plugin for the manager (https://github.com/1cge o/Ferramentas_Gerencia). DTSTAMP:20260403T193016Z LOCATION:General online SUMMARY:Geoinformation Production Management System - A QGIS based open-sou rce solution to nationwide geospatial data production management - Felipe de Carvalho Diniz\, Philipe Borba URL:https://talks.staging.osgeo.org/foss4g-2022/talk/WSHGDN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-PJDH8K@talks.staging.osgeo.org DTSTART;TZID=CET:20220826T141500 DTEND;TZID=CET:20220826T144500 DESCRIPTION:Building footprint extraction is a popular and booming research field. Annually\, several research papers are published showing deep lear ning semantic segmentation-based methods to perform this kind of automated feature extraction. Unfortunately\, many of those papers do not have open -source implementations for public usage\, making it difficult for other r esearchers to access those implementations.\n\nHaving that in mind\, we pr esent DeepLearningTools and pytorch_segmentation_models_trainer. Both are openly available implementations of deep learning-based semantic segmentat ion. This way\, we seek to strengthen the scientific community sharing our implementations.\n\nDeepLearningTools is a QGIS plugin that enables build ing and visualizing masks from vector data. Moreover\, it allows the usage of inference web services published by pytorch_segmentation_models_traine r\, creating a more feasible way for QGIS users to train Deep Learning Mod els.\n\npytorch_segmentation_models_trainer (pytorch-smt) is a Python fram ework built with PyTorch\, PyTorch-Lightning\, Hydra\, segmentation_models .pytorch\, rasterio\, and shapely. This implementation enables using YAML files to perform segmentation mask building\, model training\, and inferen ce. In particular\, it ships pre-trained models for building footprint ext raction and post-processing implementations to obtain clean geometries. In addition\, one can deploy an inference service built using FastAPI and us e it in either web-based applications or a QGIS plugin like DeepLearningTo ols.\n\nResNet-101 U-Net Frame Field\, ResNet-101 DeepLabV3+ Frame Field\, HRNet W48 OCR Frame Field\, Modified PolyMapper (ModPolyMapper)\, and Pol ygonRNN are some of the models available in pytorch-smt. These models were trained using the Brazilian Army Geographic Service Building Dataset (BAG S Dataset)\, a newly available dataset built using aerial imagery from the Brazilian States of Rio Grande do Sul and Santa Catarina. Pytorch-smt als o enables training object detection and instance segmentation tasks using concise training configuration.\n\nThis way\, considering the aforemention ed\, this talk presents the usage overview of both technologies and some d emonstrations. Using metrics like precision\, recall\, and F1\, we assess the results achieved by the implementations developed as a product of our research\, showing that they have the potential to produce vector data mor e efficiently than manual acquisition methods.\n\nDeepLearningTools is ava ilable at the QGIS plugin repository\, while pytorch_segmentation_models_t rainer is available at Python Package Manager (pip). The Brazilian Army Ge ographic Service develops both solutions\, making their codes available at https://github.com/phborba/DeepLearningTools and https://github.com/phbor ba/pytorch_segmentation_models_trainer. DTSTAMP:20260403T193016Z LOCATION:Room 4 SUMMARY:Building Footprint Extraction in Vector Format Using pytorch_segmen tation_models_trainer\, QGIS Plugin DeepLearningTools and The Brazilian Ar my Geographic Service Building Dataset - Philipe Borba URL:https://talks.staging.osgeo.org/foss4g-2022/talk/PJDH8K/ END:VEVENT END:VCALENDAR