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Cloud Computing Harmonization for Earth Observation GEO Data

Learn about achieving interoperability in cloud computing for earth observation data. Explore formal and de-facto standards, challenges, and solutions for cross-cloud platform compatibility via openEO. Get involved now!

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Cloud Computing Harmonization for Earth Observation GEO Data

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  1. Interoperability and Harmonization in Cloud Computing for Earth Observation GEO Data Technology Workshop Edzer Pebesma / Wed, 24 April / Vienna, Austria @edzerpebesma / edzer.pebesma@uni-muenster.de

  2. According to http://interoperability-definition.info/en/ , and also used by Wikipedia: Interoperability is a characteristic of a product or system, whose interfaces are completely understood, to work with other products or systems, present or future, in either implementation or access, without any restrictions.

  3. How is interoperability achieved? 1. by adopting formal standards (e.g. from the OGC) 2. by adopting de-facto standards (e.g. existing OS software) 3. by developing new versions of 2. (and possibly 1.)

  4. 1: formal standards, some examples 1. OGC WPS (complex, not dedicated: benefits?) 2. OGC WCS (complex; download, not for cloud processing) 3. OGC WCPS (complex but too simple; a good idea but restrictions are: vendor lock-in, need to mosaic everything to coverages first)

  5. 2: de-facto standards, some examples

  6. 3: why invent something new? Cross-cloud platform interoperability is missing: 1. comparing a process on say: Sentinel hub, GEE, and ODC is - so much work that nobody will undertake it - when done, very hard to communicate (who listens?) 2. this is bad for science: no validation, no reproducibility 3. no possibility to easily compare capability and pricing 4. difficult to combine different cloud processing systems

  7. openEO openEO.org implements an API for cross-cloud platform interoperability:

  8. openEO data model Raster and vector data (hyper)cube view

  9. Data cube view? - user defines coord. reference system, extent and resolution - does not prescribe a particular storage model - deals with image collections, coverages, databases - resamples on-the-fly in space and time - previews low-resolution, allowing interactivity - evaluates lazily: only pixels needed are processed (plot, download)

  10. openEO.org - implements 3 clients (JavaScript, R, Python) and 7 back-ends (Sentinel hub, GeoPySpark, OpenShift, GRASS GIS, WCPS, GEE, R) - enables queries like: - on which cloud can I run this process on these data? - and what will that cost? - uses Open API, STAC, is open source - aims at becoming a community project - invites YOU to get involved, NOW!

  11. Contact edzer.pebesma@uni-muenster.de @edzerpebesma https://openeo.org/

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