Jancauskas, Vytautas und Traoré, Kalifou René und Belmonte, Juan Pablo Espejo und Espinoza Molina, Daniela (2025) Grünblick - AI powered forest biomass estimation service. In: Proceedings of the 2025 conference on Big Data from Space (BiDS'25), Seiten 101-105. Big Data from Space (BiDS'25), 2025-10-28 - 2025-10-30, Oberpfaffenhofen. doi: 10.2760/2119408. ISBN 978-92-68-31935-2. (im Druck)
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Offizielle URL: https://op.europa.eu/en/publication-detail/-/publication/f678733c-a269-11f0-97c8-01aa75ed71a1/language-en
Kurzfassung
Accurate forest biomass estimation is essential for climate change mitigation, biodiversity monitoring, and sustainable forest management. Recent advances in remote sensing and machine learning have opened new avenues for large-scale, high-resolution biomass mapping. In this work, we introduce Grunblick, an AI-powered service designed for scalable forest biomass estimation, leveraging multi-sensor Earth Observation (EO) data, including Sentinel-1 and Sentinel-2 imagery. The Grunblick pipeline integrates modular deep learning models, notably U-Net architectures with interchangeable feature extraction backbones, to perform pixel-wise above-ground biomass (AGB) regression. We validate our system using the public Biomassters benchmark, demonstrating significant performance gains through multi-modal sensor fusion and self-supervised pretraining strategies. Future extensions will include uncertainty quantification and global deployment capabilities.
| elib-URL des Eintrags: | https://elib.dlr.de/223307/ | ||||||||||||||||||||
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| Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
| Titel: | Grünblick - AI powered forest biomass estimation service | ||||||||||||||||||||
| Autoren: |
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| Datum: | 2025 | ||||||||||||||||||||
| Erschienen in: | Proceedings of the 2025 conference on Big Data from Space (BiDS'25) | ||||||||||||||||||||
| Referierte Publikation: | Nein | ||||||||||||||||||||
| Open Access: | Nein | ||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||||||
| DOI: | 10.2760/2119408 | ||||||||||||||||||||
| Seitenbereich: | Seiten 101-105 | ||||||||||||||||||||
| ISBN: | 978-92-68-31935-2 | ||||||||||||||||||||
| Status: | im Druck | ||||||||||||||||||||
| Stichwörter: | AI Software Toolkit, Biomass Estimation, Remote Sensing. | ||||||||||||||||||||
| Veranstaltungstitel: | Big Data from Space (BiDS'25) | ||||||||||||||||||||
| Veranstaltungsort: | Oberpfaffenhofen | ||||||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
| Veranstaltungsbeginn: | 28 Oktober 2025 | ||||||||||||||||||||
| Veranstaltungsende: | 30 Oktober 2025 | ||||||||||||||||||||
| Veranstalter : | Joint Research Centre (JRC), European Commission | ||||||||||||||||||||
| 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: | Traoré, Mr René | ||||||||||||||||||||
| Hinterlegt am: | 13 Mär 2026 09:23 | ||||||||||||||||||||
| Letzte Änderung: | 13 Mär 2026 09:23 |
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