Goyal, Shivam und Karmakar, Chandrabali und Camero, Andres und Dumitru, Corneliu Octavian und Datcu, M. (2025) Ulearn: An explainable uncertainty-aware machine learning tool for unsupervised classification. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 1-4. IEEE Geoscience and Remote Sensing Society (GRSS). IGARSS 2025, 2025-08-03 - 2025-08-08, Brisbane, Australia. (im Druck)
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Offizielle URL: https://www.2025.ieeeigarss.org/view_paper.php?PaperNum=5278&SessionID=1416
Kurzfassung
Research and application combining machine and deep learning algorithms and earth observation data has seen tremendous success in the last decades. However, most popular models are black-boxes. A scarcity of eXplainable unsupervised models with adequate accuracy and robustness is still evident. In this research we propose a tool to make unsupervised classification of image data with an eXplainable probabilistic model which makes it feasible for domain experts to validate the results of the classification, especially when the model is not adequately certain of its predictions. The eXplainable model used is a Bayesian generative model called Latent Dirichlet Allocation (LDA). LDA has recently been established as an eXplainable model and able to process diverse data types e.g., text, image. Audio etc. We propose a user interface tool to facilitate using LDA for images classification. We present two use-cases with the tool: 1) automatic coastline change detection with Sentinel-2 images at Baltic Sea, and 2) fire detection at Los Angeles.
elib-URL des Eintrags: | https://elib.dlr.de/214935/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||||||
Zusätzliche Informationen: | This work is partially funded by the HGF under AutoCoast project (grant ZT-I-PF-4048) | ||||||||||||||||||||||||
Titel: | Ulearn: An explainable uncertainty-aware machine learning tool for unsupervised classification | ||||||||||||||||||||||||
Autoren: |
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Datum: | August 2025 | ||||||||||||||||||||||||
Erschienen in: | International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||||||||||
Verlag: | IEEE Geoscience and Remote Sensing Society (GRSS) | ||||||||||||||||||||||||
Status: | im Druck | ||||||||||||||||||||||||
Stichwörter: | LDA, Sentinel-2, Fire detection, Coastal change, eXplainable Machine Learning Tool, Uncertainty | ||||||||||||||||||||||||
Veranstaltungstitel: | IGARSS 2025 | ||||||||||||||||||||||||
Veranstaltungsort: | Brisbane, Australia | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 3 August 2025 | ||||||||||||||||||||||||
Veranstaltungsende: | 8 August 2025 | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||
Hinterlegt von: | Dumitru, Corneliu Octavian | ||||||||||||||||||||||||
Hinterlegt am: | 09 Jul 2025 11:39 | ||||||||||||||||||||||||
Letzte Änderung: | 12 Aug 2025 17:07 |
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