Geiß, Christian und Aravena Pelizari, Patrick und Marconcini, Mattia und Sengara, Wayan und Edwards, Mark und Lakes, Tobia und 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, Seiten 175-188. Elsevier. doi: 10.1016/j.isprsjprs.2014.07.016. ISSN 0924-2716.
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Offizielle URL: http://www.sciencedirect.com/science/article/pii/S0924271614002007
Kurzfassung
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 However, for numerous urban areas in earthquake prone regions this information is mostly outdated, unavailable, or simply not existent.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.
elib-URL des Eintrags: | https://elib.dlr.de/96094/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | Juni 2015 | ||||||||||||||||||||||||||||||||
Erschienen in: | ISPRS Journal of Photogrammetry and Remote Sensing | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 104 | ||||||||||||||||||||||||||||||||
DOI: | 10.1016/j.isprsjprs.2014.07.016 | ||||||||||||||||||||||||||||||||
Seitenbereich: | Seiten 175-188 | ||||||||||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||||||||||
ISSN: | 0924-2716 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | Seismic building structural types Very high and medium resolution imagery Machine learning SVM Random Forests Earthquake loss estimation | ||||||||||||||||||||||||||||||||
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 - Vorhaben Zivile Kriseninformation und Georisiken (alt), R - Vorhaben Fernerkundung der Landoberfläche (alt) | ||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit Deutsches Fernerkundungsdatenzentrum > Landoberfläche | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Geiß, Christian | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 03 Jun 2015 08:59 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 06 Nov 2023 09:26 |
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