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Developing a reusable infrastructure for machine learning on diverse earth observation data for sustainable agriculture and forestry

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dc.contributor.authorMcClelland, Jennifer
dc.contributor.authorDomenic, Anto Raja
dc.contributor.authorBeyer, Florian
dc.contributor.authorde Kock, Arno
dc.contributor.authorGolla, Burkhard
dc.contributor.editorDörr, Jörg
dc.contributor.editorSteckel, Thilo
dc.date.accessioned2025-02-04T14:38:01Z
dc.date.available2025-02-04T14:38:01Z
dc.date.issued2025
dc.description.abstractThe integration of machine learning (ML) into environmental protection, particularly in sustainable agriculture and forestry, is increasingly vital given the spatio-temporal scale of the data and analysis. Earth observation data from Sentinel-1 (S1), Sentinel-2 (S2), weather, and LiDAR provide valuable insights, but applying ML algorithms to these diverse datasets presents challenges due to their differences in data structure and formats as well as spatial, spectral and temporal resolutions. This research develops a multi-purpose, extensible infrastructure using open-source technologies, implemented within the cloud platform CODE-DE at Julius Kühn-Institut (JKI), to streamline ML applications for geo-located earth observation data. The infrastructure supports diverse data types, including satellite, weather, and LiDAR records, and is adaptable to future ML models. It has been rigorously tested for detecting plant growth stages (BBCH), demonstrating its potential in agricultural analysis. Future work will extend this to detecting tree and shrub growth events. This research contributes to sustainable agriculture by advancing reusable ML solutions for environmental monitoring.en
dc.identifier.doi10.18420/giljt2025_37
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-802-6
dc.identifier.pissn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45696
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 358
dc.subjectmachine learning
dc.subjectearth observation data
dc.subjectsustainable agriculture
dc.subjectforestry
dc.subjectopen source technologies
dc.subjectSentinel-1
dc.subjectSentinel-2
dc.subjectLiDAR
dc.subjectCODE-DE Platform
dc.titleDeveloping a reusable infrastructure for machine learning on diverse earth observation data for sustainable agriculture and forestryen
dc.typeText/Conference Paper
gi.citation.endPage332
gi.citation.publisherPlaceBonn
gi.citation.startPage327
gi.conference.date25/26. Februar 2025
gi.conference.locationWieselburg, Austria
gi.conference.reviewfull

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P358 - 45. GIL-Jahrestagung 2025 - Fokus: Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft