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

Wurm, Michael and Droin, Ariane and Stark, Thomas and Geiß, Christian and Sulzer, Wolfgang and 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), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/ijgi10010023. ISSN 2220-9964.

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Official URL: https://www.mdpi.com/2220-9964/10/1/23/htm

Abstract

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.

Item URL in elib:https://elib.dlr.de/140304/
Document Type:Article
Title:Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Droin, ArianeAriane.Droin (at) dlr.deUNSPECIFIED
Stark, ThomasThomas.Stark (at) dlr.dehttps://orcid.org/0000-0002-6166-7541
Geiß, ChristianChristian.Geiss (at) dlr.deUNSPECIFIED
Sulzer, Wolfgangwolfgang.sulzer (at) uni-graz.athttps://orcid.org/0000-0001-6040-2405
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Date:12 January 2021
Journal or Publication Title:ISPRS International Journal of Geo-Information
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.3390/ijgi10010023
Page Range:pp. 1-20
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2220-9964
Status:Published
Keywords:deep learning, dsm, building stock model, building types, energy modeling,
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Wurm, Michael
Deposited On:14 Jan 2021 15:24
Last Modified:14 Jan 2021 15:24

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