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Article

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

1
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Wessling, Germany
2
Institute for Geography and Regional Planning, University of Graz, Heinrichstr. 36, 8010 Graz, Austria
3
Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010023
Received: 24 November 2020 / Revised: 27 December 2020 / Accepted: 11 January 2021 / Published: 12 January 2021
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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 DL-building 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. View Full-Text
Keywords: building stock model; building type; deep learning; heat demand modeling; digital surface model; aerial image building stock model; building type; deep learning; heat demand modeling; digital surface model; aerial image
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MDPI and ACS Style

Wurm, M.; Droin, A.; Stark, T.; Geiß, C.; Sulzer, W.; Taubenböck, H. Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling. ISPRS Int. J. Geo-Inf. 2021, 10, 23. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010023

AMA Style

Wurm M, Droin A, Stark T, Geiß C, Sulzer W, Taubenböck H. Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling. ISPRS International Journal of Geo-Information. 2021; 10(1):23. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010023

Chicago/Turabian Style

Wurm, Michael, Ariane Droin, Thomas Stark, Christian Geiß, Wolfgang Sulzer, and Hannes Taubenböck. 2021. "Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling" ISPRS International Journal of Geo-Information 10, no. 1: 23. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010023

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