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UID:pretalx-foss4g-2024-academic-track-FPQDTF@talks.staging.osgeo.org
DTSTART;TZID=-03:20241204T140000
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DESCRIPTION:Application-oriented research projects often involve diverse co
 nsortium members\, from universities to research institutions\, to authori
 ties\, to domain end users. This requires the integration of very heteroge
 neous data sources\, the facilitation of their combined processing\, and t
 he presentation of the results in adequate ways. The challenge here is not
  only the technical realisation itself\, but maybe even more to design sol
 utions catering to this wide user group\, balancing feature-richness with 
 easy usability. Data is abundant\, and processing plentiful\, but it all n
 eeds to go the last mile to the final user.\n\nOne such research project i
 s “AgriSens DEMMIN 4.0”\, which is advancing remote sensing for the di
 gitalisation in agricultural crop production. Thus\, the user group includ
 es programmers\, domain scientists\, as well as farmers. Until now\, agric
 ulture is not yet widely taking advantage of EO products. Therefore\, the 
 project not only addresses the creation of novel remote-sensing-based appl
 ication techniques and their implementation\, but puts an equally distinct
  emphasis on the development of an accompanying data integration and visua
 lisation system. In this work\, we describe how our architecture – consi
 sting of several pieces of free and open source geo software – closes th
 e gap between data providers and information consumers as it facilitates n
 ecessary analysis steps and combines these with adequate presentation to d
 ecision makers.\n\nIn our IT architecture\, we utilise one central datacub
 e to conquer this problem\, which acts as a cloud-based geospatial data ho
 lding and computation platform. It gathers a multitude of data\, ranging f
 rom optical and radar raster imagery through weather data to in-situ field
  measurements\, and pre-processes it into an interoperable\, analysis-read
 y state. These resources can then be accessed through APIs for external us
 age\, or computations can be carried out directly on the datacube and the 
 results immediately visualised with tools hosted on the same server.\n\nTh
 e whole system is located at the Leibniz Supercomputing Centre of the Bava
 rian Academy of Sciences and Humanities (LRZ). Apart from utilising the co
 mputing resources available there\, this also opens up synergies with alre
 ady-existing projects: We can make use of the enormous amount of EO data t
 hat is already available within the LRZ’s “Data Science Storage” and
  the DLR’s “terrabyte” platform. These storages are directly mounted
  into our server so that the datacube can access petabytes of imagery with
 out having to duplicate it again\, saving costs and emissions.\n\nAt the c
 ore of our infrastructure is an instance of the “Open Data Cube” (ODC)
  software package. Metadata is ingested into its PostgreSQL database and c
 an be retrieved via the built-in web-based data discovery application “O
 DC Explorer” or via an API endpoint of the emergent STAC standard (which
  in turn can be accessed via the “STAC Browser” web application or any
  other compatible software). All raster data is provided in the Cloud-Opti
 mised GeoTIFF (COG) format\, allowing efficient access even from remote ma
 chines.\n\nOur main interface for scientific computation is Jupyter Hub\, 
 enabling collaborative work across institutions. For each user\, a dedicat
 ed Jupyter Lab instance is spawned in its own Docker container that can ac
 cess all the data of the previously mentioned storages and has a certain a
 mount of computing resources allocated to it. Users can write their code i
 n Python or R\, arguably the most popular programming languages in the EO 
 community\, which offer straightforward packages for connecting to an ODC\
 , namely “datacube” and “odcR”. This way\, scientists receive the 
 typical professional online analysis environment in which they can work cl
 osely to the data and use all the powers of these programming languages an
 d their EO-friendly ecosystem.\n\nAnother option to work with the data is 
 via openEO\, the new standardised way to interact with big EO data cloud p
 rocessing backends. We incorporate this standard by utilising the “openE
 O Spring Driver”\, an adapter to translate user-submitted openEO process
  graphs into analysis code that can be run using ODC. For compatibility wi
 th legacy software\, it is also possible to request rendered images of pre
 -configured analysis algorithms via WMS\, which are being served by an ins
 tance of the powerful “datacube-ows” package.\n\nThe final goal\, howe
 ver\, is to connect farmers and other end users to these results\, who can
 not or do not want to deal with complex interfaces. Therefore\, these high
 ly technical tools are not sufficient\, but need to be accompanied by easy
 -to-use graphical interfaces. Drawing on the possibilities of modern web t
 echnologies\, we realise these through purpose-built web apps. Arranged ar
 ound an OpenLayers-powered map component\, data products are streamed in C
 OG format from the datacube and displayed along with the needed additional
  tooling for interpretation. For example\, in this fashion we realised a d
 emonstrator showcasing the results of a water balance model for irrigated 
 potato fields.\n\nAnother outlet of the project is the “FieldMApp”\, a
  mobile application designed to be used by farmers both in the field as we
 ll as in the office to digitise and monitor areas of lower yield within cr
 op fields. For evaluating plant vitality\, a vegetation-index-based raster
  product is calculated on the datacube using the latest Sentinel-2 imagery
  and long-term crop-specific averages. Due to the tablet application being
  programmed with the platform-agnostic Flutter framework\, but its standar
 d mapping component not yet supporting COGs\, it was necessary to resort t
 o WMS for serving the raster data. As mentioned above\, this is comfortabl
 y possible by configuring “datacube-ows” on top of ODC.\n\nOverall\, t
 he challenge of interoperating various data supplies\, processing chains a
 nd custom-tailored interfaces – as typically encountered in interdiscipl
 inary research projects – requires complex solutions\, but can be achiev
 ed quite well by utilising a datacube approach with free and open source g
 eo software building blocks. Our integrated system successfully demonstrat
 es such a use case for the domain of remote sensing in agriculture.
DTSTAMP:20260513T151635Z
LOCATION:Room III
SUMMARY:Integrating\, Processing and Presenting Big Geodata with Earth Obse
 rvation Datacubes in an Interdisciplinary Research Context - Christoph Fri
 edrich
URL:https://talks.staging.osgeo.org/foss4g-2024-academic-track/talk/FPQDTF/
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