Persello, Claudio und Wegner, Jan Dirk und Hänsch, Ronny und Tuia, Devis und Ghamisi, Pedram und Koeva, Mila und Camps-Valls, Gustau (2022) Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities. IEEE Geoscience and Remote Sensing Magazine (GRSM), 10 (2), Seiten 172-200. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2021.3136100. ISSN 2168-6831.
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Offizielle URL: https://ieeexplore.ieee.org/abstract/document/9681713
Kurzfassung
The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.
elib-URL des Eintrags: | https://elib.dlr.de/191309/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | Juni 2022 | ||||||||||||||||||||||||||||||||
Erschienen in: | IEEE Geoscience and Remote Sensing Magazine (GRSM) | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 10 | ||||||||||||||||||||||||||||||||
DOI: | 10.1109/MGRS.2021.3136100 | ||||||||||||||||||||||||||||||||
Seitenbereich: | Seiten 172-200 | ||||||||||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||||||||||
ISSN: | 2168-6831 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | Deep Learning, Earth Observation, Sustainable Development Goals | ||||||||||||||||||||||||||||||||
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 Hochfrequenztechnik und Radarsysteme > SAR-Technologie | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Hänsch, Ronny | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 01 Dez 2022 13:15 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 19 Jul 2023 08:53 |
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