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Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques

Geiß, Christian and Aravena Pelizari, Patrick and Marconcini, Mattia and Sengara, Wayan and Edwards, Mark and Lakes, Tobia and Taubenböck, Hannes (2015) Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 104, pp. 175-188. Elsevier. ISSN 0924-2716

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Official URL: http://www.sciencedirect.com/science/article/pii/S0924271614002007

Abstract

Detailed information about seismic building structural types (SBSTs) is crucial for accurate earthquake vulnerability and risk modeling as it reflects the main load-bearing structures of buildings and, thus, the behavior under seismic load. However, for numerous urban areas in earthquake prone regions this information is mostly outdated, unavailable, or simply not existent. To this purpose, we present an effective approach to estimate SBSTs by combining scarce in situ observations, multi-sensor remote sensing data and machine learning techniques. In particular, an approach is introduced, which deploys a sequential procedure comprising five main steps, namely calculation of features from remote sensing data, feature selection, outlier detection, generation of synthetic samples, and supervised classification under consideration of both Support Vector Machines and Random Forests. Experimental results obtained for a representative study area, including large parts of the city of Padang (Indonesia), assess the capabilities of the presented approach and confirm its great potential for a reliable area-wide estimation of SBSTs and an effective earthquake loss modeling based on remote sensing, which should be further explored in future research activities.

Item URL in elib:https://elib.dlr.de/96094/
Document Type:Article
Title:Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
Aravena Pelizari, PatrickPatrick.AravenaPelizari (at) dlr.deUNSPECIFIED
Marconcini, MattiaMattia.Marconcini (at) dlr.deUNSPECIFIED
Sengara, Wayaniws (at) geotech.pauir.itb.ac.idUNSPECIFIED
Edwards, Markmark.edwards (at) ga.gov.auUNSPECIFIED
Lakes, Tobiatobia.lakes (at) geo.hu-berlin.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:June 2015
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:104
Page Range:pp. 175-188
Publisher:Elsevier
ISSN:0924-2716
Status:Published
Keywords:Seismic building structural types Very high and medium resolution imagery Machine learning SVM Random Forests Earthquake loss estimation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Zivile Kriseninformation und Georisiken (old), R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
German Remote Sensing Data Center > Land Surface
Deposited By: Geiß, Christian
Deposited On:03 Jun 2015 08:59
Last Modified:06 Sep 2019 15:28

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