Stark, Thomas und Wurm, Michael und Stokes, Eleanor und Seto, Karen C. und Taubenböck, Hannes (2025) GDP Estimation using a Deep Learning Fusion Model for Multi-Source Remote Sensing Data. In: 2025 Joint Urban Remote Sensing Event, JURSE 2025, Seiten 1-4. IEEE. 2025 Joint Urban Remote Sensing Event (JURSE), 2025-05-05 - 2025-05-07, Tunis. doi: 10.1109/JURSE60372.2025.11076044. ISBN 979-8-3503-7183-3. ISSN 2642-9535.
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Offizielle URL: https://ieeexplore.ieee.org/document/11076044
Kurzfassung
In many developing countries, obtaining accurate and high-resolution GDP estimates is crucial for guiding economic policy and challenging due to limited data availability. This study introduces a novel deep learning fusion model to estimate the aggregated values of the Gross Domestic Product (GDP) in Brazil on a high spatial resolution grid of 1 km2. This is done by a combination of remote sensing data, specifically optical imagery and nighttime light emissions. The model processes these data streams separately before fusing them for GDP prediction. This approach allows for the extraction of both physical and socioeconomic features relevant to economic activity, providing valuable insights for economic planning and policy making. Our fusion model achieved high r2 values of up to 0.75 and was trained and tested in 29 Brazilian cities, demonstrating its effectiveness and scalability for large-scale urban economic estimations.
elib-URL des Eintrags: | https://elib.dlr.de/215453/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||
Titel: | GDP Estimation using a Deep Learning Fusion Model for Multi-Source Remote Sensing Data | ||||||||||||||||||||||||
Autoren: |
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Datum: | 16 Juli 2025 | ||||||||||||||||||||||||
Erschienen in: | 2025 Joint Urban Remote Sensing Event, JURSE 2025 | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
DOI: | 10.1109/JURSE60372.2025.11076044 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||||||
ISSN: | 2642-9535 | ||||||||||||||||||||||||
ISBN: | 979-8-3503-7183-3 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | GDP Estimation, Remote Sensing, Deep Learning | ||||||||||||||||||||||||
Veranstaltungstitel: | 2025 Joint Urban Remote Sensing Event (JURSE) | ||||||||||||||||||||||||
Veranstaltungsort: | Tunis | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 5 Mai 2025 | ||||||||||||||||||||||||
Veranstaltungsende: | 7 Mai 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 - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||
Hinterlegt von: | Stark, Thomas | ||||||||||||||||||||||||
Hinterlegt am: | 31 Jul 2025 08:29 | ||||||||||||||||||||||||
Letzte Änderung: | 04 Sep 2025 14:27 |
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