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Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings

Aravena Pelizari, Patrick and Geiß, Christian and Schoepfer, Elisabeth and Riedlinger, Torsten and Aguirre, Paula and Santa Maria, Hernan and Merino Pena, Yvonne and Gómez Zapata, Camilo and Pittore, Massimiliano and Taubenböck, Hannes (2021) Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings. EGU General Assembly 2021, 19–30 Apr 2021, Wien, Österreich.

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Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of in-situ data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of in-situ information on exposed buildings.

Item URL in elib:https://elib.dlr.de/144765/
Document Type:Conference or Workshop Item (Speech)
Title:Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Aravena Pelizari, PatrickPatrick.AravenaPelizari (at) dlr.deUNSPECIFIED
Geiß, Christianchristian.geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553
Schoepfer, Elisabethelisabeth.schoepfer (at) dlr.dehttps://orcid.org/0000-0002-6496-4744
Riedlinger, TorstenTorsten.Riedlinger (at) dlr.deUNSPECIFIED
Aguirre, Paulaaguirre.paula (at) gmail.comUNSPECIFIED
Santa Maria, Hernanhsm (at) ing.puc.clUNSPECIFIED
Gómez Zapata, Camilojcgomez (at) gfz-potsdam.deUNSPECIFIED
Pittore, MassimilianoMassimiliano.Pittore (at) eurac.eduUNSPECIFIED
Taubenböck, HannesHannes.Taubenboeck (at) dlr.deUNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:seismic risk assessment, image classification, deep learning
Event Title:EGU General Assembly 2021
Event Location:Wien, Österreich
Event Type:international Conference
Event Dates:19–30 Apr 2021
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: Geiß, Christian
Deposited On:02 Nov 2021 20:34
Last Modified:02 Nov 2021 20:34

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