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A multi-talented datacube: integrating, processing and presenting big geodata for the agricultural end user

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dc.contributor.authorFriedrich, Christoph
dc.contributor.authorLöw, Johannes
dc.contributor.authorOtte, Insa
dc.contributor.authorHill, Steven
dc.contributor.authorFörtsch, Sebastian
dc.contributor.authorSchwalb-Willmann, Jakob
dc.contributor.authorGessner, Ursula
dc.contributor.authorSchierghofer, Christoph
dc.contributor.authorTruckenbrodt, Sina
dc.contributor.authorSchonert, Eric
dc.contributor.authorPiernicke, Thomas
dc.contributor.authorAssmann, Denise
dc.contributor.authorConrad, Christopher
dc.contributor.authorThiel, Michael
dc.date.accessioned2024-04-08T11:56:34Z
dc.date.available2024-04-08T11:56:34Z
dc.date.issued2024
dc.description.abstractWhile scientific methods leveraging Earth Observation for agriculture are abundant, their actual application in Germany remains scarce. A key challenge in this context is to connect the end users to the data without the many technical obstacles. Therefore, we present a versatile platform that not only integrates and processes big geodata of highly diverse origin and type, but also provides access to these resources in ways that reflect the individual user’s requirements and expertise. Based on free and open-source software building blocks, our datacube facilitates scientific computation through R and Python environments or direct API access, including emergent technologies such as openEO, STAC, and COG. At the same time, the results are delivered to easy-to-use applications that adequately present them to non-technical experts. We detail the architecture of the system and demonstrate a use case serving computed plant vitality information directly to farmers in the field.en
dc.identifier.doi10.18420/giljt2024_36
dc.identifier.isbn978-3-88579-738-8
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43882
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectanalysis-ready data
dc.subjectcloud processing
dc.subjectinteroperability
dc.subjectdata access
dc.subjectuser interfaces
dc.titleA multi-talented datacube: integrating, processing and presenting big geodata for the agricultural end useren
dc.typeText/Conference Paper
gi.citation.endPage256
gi.citation.publisherPlaceBonn
gi.citation.startPage251
gi.conference.date27.-28. Februar 2024
gi.conference.locationStuttgart
gi.conference.reviewfull

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P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft