Zhao, Daixin und Xiao, Tianqi und Izadgoshasb, Hamed und Wickert, Jens und Kuzu, Ridvan Salih und Asgarimehr, Milad (2025) EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response. Helmholtz AI Conference 2025, 2025-06-03 - 2025-06-05, Karlsruhe, Germany. (im Druck)
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Kurzfassung
With growing concerns about climate change, increasing natural hazards, and extreme weather events, monitoring Earth’s surface parameters has become a critical area of interest for both the scientific community and society. Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative and low-cost technique that exploits existing Global Navigation Satellite System (GNSS) signals after reflection from Earth’s surface. GNSS-R constellations offer unique observations with unprecedented data volume, temporal resolution, and spatial coverage across the entire globe under all-weather conditions. As the data volumes are continuously accumulating, the trend in applying Artificial Intelligence (AI) is expanding. However, current AI models rely heavily on labelled data, feature engineering, and extra fine-tuning, leading to high computational and labor costs. To address these issues, we propose the project EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response. EcoGEM develops cutting-edge Earth observation foundation models using GNSS-R measurements and integrates them with other remote sensing data. It pioneers the first general-purpose GNSS-R foundation models and curated multimodal datasets to support climate science, hazard detection, and environmental monitoring. Unlike task-specific methods, the proposed models adapt across applications such as soil moisture, vegetation water content, and ocean wind speed. Uniquely, EcoGEM emphasizes energy-efficient AI through model pruning, knowledge distillation, and dynamic architectures, enabling deployment on edge devices and small satellite platforms. This collaborative project of GFZ and DLR advances sustainable AI and promotes novel and open-access tools for Earth scientists, environmental policymakers, and global users.
elib-URL des Eintrags: | https://elib.dlr.de/214111/ | ||||||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||||||||||
Zusätzliche Informationen: | This work is part of the EcoGEM project funded by the Helmholtz Association of German Research Centres (HGF), contract number ZT-I-PF-5-243. | ||||||||||||||||||||||||||||
Titel: | EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 3 Juni 2025 | ||||||||||||||||||||||||||||
Referierte Publikation: | Nein | ||||||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||||||
Status: | im Druck | ||||||||||||||||||||||||||||
Stichwörter: | GNSS Reflectometry, Earth Observation, Artificial Intelligence, Multimodal Remote Sensing, Environmental Monitoring, Energy-efficient Models, Hazard Detection, Foundation Models | ||||||||||||||||||||||||||||
Veranstaltungstitel: | Helmholtz AI Conference 2025 | ||||||||||||||||||||||||||||
Veranstaltungsort: | Karlsruhe, Germany | ||||||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||||||
Veranstaltungsbeginn: | 3 Juni 2025 | ||||||||||||||||||||||||||||
Veranstaltungsende: | 5 Juni 2025 | ||||||||||||||||||||||||||||
Veranstalter : | Helmholtz Artificial Intelligence Cooperation Unit | ||||||||||||||||||||||||||||
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 - Optische Fernerkundung, R - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||||||
Hinterlegt von: | Kuzu, Dr. Ridvan Salih | ||||||||||||||||||||||||||||
Hinterlegt am: | 06 Jun 2025 10:28 | ||||||||||||||||||||||||||||
Letzte Änderung: | 06 Jun 2025 10:28 |
Verfügbare Versionen dieses Eintrags
- EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response. (deposited 06 Jun 2025 10:28) [Gegenwärtig angezeigt]
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