Wurm, Michael und Droin, Ariane und Stark, Thomas und Geiß, Christian und Sulzer, Wolfgang und Taubenböck, Hannes (2021) Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling. ISPRS International Journal of Geo-Information, 10 (23), Seiten 1-20. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/ijgi10010023. ISSN 2220-9964.
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Offizielle URL: https://www.mdpi.com/2220-9964/10/1/23/htm
Kurzfassung
Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DLbuilding extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.
elib-URL des Eintrags: | https://elib.dlr.de/140304/ | ||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||
Titel: | Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 12 Januar 2021 | ||||||||||||||||||||||||||||
Erschienen in: | ISPRS International Journal of Geo-Information | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
Band: | 10 | ||||||||||||||||||||||||||||
DOI: | 10.3390/ijgi10010023 | ||||||||||||||||||||||||||||
Seitenbereich: | Seiten 1-20 | ||||||||||||||||||||||||||||
Verlag: | Multidisciplinary Digital Publishing Institute (MDPI) | ||||||||||||||||||||||||||||
ISSN: | 2220-9964 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | deep learning, dsm, building stock model, building types, energy modeling, | ||||||||||||||||||||||||||||
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: | Wurm, Michael | ||||||||||||||||||||||||||||
Hinterlegt am: | 14 Jan 2021 15:24 | ||||||||||||||||||||||||||||
Letzte Änderung: | 28 Mär 2023 23:58 |
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