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Sun, Wenlu und Sun, Yao und Liu, Chenying und Albrecht, Conrad M (2023) DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 3616-3619. IGARSS 2023, 2023-07-16 - 2023-07-21, Pasadena, CA, USA. doi: 10.1109/IGARSS52108.2023.10281573.
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Offizielle URL: https://ieeexplore.ieee.org/document/10281573
Kurzfassung
Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.
| elib-URL des Eintrags: | https://elib.dlr.de/195495/ | ||||||||||||||||||||
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| Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||
| Zusätzliche Informationen: | latest version available as https://arxiv.org/abs/2306.06269 | ||||||||||||||||||||
| Titel: | DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience | ||||||||||||||||||||
| Autoren: |
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| Datum: | 2023 | ||||||||||||||||||||
| Erschienen in: | International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||
| Open Access: | Ja | ||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||
| In SCOPUS: | Ja | ||||||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||||||
| DOI: | 10.1109/IGARSS52108.2023.10281573 | ||||||||||||||||||||
| Seitenbereich: | Seiten 3616-3619 | ||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||
| Stichwörter: | urban planning, local climate zones, climate resilience, LiDAR, Landsat 8, deep neural network architecture, explainable artificial intelligence | ||||||||||||||||||||
| Veranstaltungstitel: | IGARSS 2023 | ||||||||||||||||||||
| Veranstaltungsort: | Pasadena, CA, USA | ||||||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
| Veranstaltungsbeginn: | 16 Juli 2023 | ||||||||||||||||||||
| Veranstaltungsende: | 21 Juli 2023 | ||||||||||||||||||||
| 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: | Albrecht, Conrad M | ||||||||||||||||||||
| Hinterlegt am: | 22 Jun 2023 13:41 | ||||||||||||||||||||
| Letzte Änderung: | 01 Sep 2024 03:00 |
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