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Learning from hyperspectral remote sensing data for machine learning algorithms in earth science

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dc.contributor.authorChristoph Jörges, Sandra Dotzler
dc.date.accessioned2024-04-08T11:56:34Z
dc.date.available2024-04-08T11:56:34Z
dc.date.issued2024
dc.description.abstractMachine Learning in Earth sciences heavily depends on sufficient training data for proper generalization. Since in-situ ground truth data is rarely available and cost-intensive to obtain, this study presents a new approach of deriving training data from hyperspectral remote sensing satellites by physical spectral signatures to use them for data-driven models with operationally available multispectral data. Examples include monitoring of crop rotation, winter greening, soil organic matter, and detection of plastic covered greenhouses (PCGs).en
dc.identifier.doi10.18420/giljt2024_61
dc.identifier.isbn978-3-88579-738-8
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43892
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.subjecthyperspectral imaging
dc.subjectplasticulture
dc.subjectmachine learning
dc.subjectland use classification
dc.subjectremote sensing
dc.titleLearning from hyperspectral remote sensing data for machine learning algorithms in earth scienceen
dc.typeText/Conference Paper
gi.citation.endPage310
gi.citation.publisherPlaceBonn
gi.citation.startPage305
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