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Estimation of seismic building structural types using remote sensing and machine learning

Aravena Pelizari, Patrick (2013) Estimation of seismic building structural types using remote sensing and machine learning. Masterarbeit, Ludwig-Maximilians-Universität.

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Kurzfassung

The current trend of urbanization leads to an increase of seismic vulnerability in earthquake prone regions. There is great demand for methods contributing to a comprehensive analysis of seismic vulnerability to face the urgent challenges of mitigation and catastrophe management. Remote sensing has high potential to contribute to an area-wide and up-to-date assessment of seismic vulnerability. For an estimation of building stock damage the built-inventory is generally categorized into different seismic building structural types, representing a construction's seismical behavior. This study reveals indirect correlations between remotely sensed data and seismic building structural types, which enable a supervised classification. Site of research is the City of Padang, Indonesia, whose urban environment is characterized by 145 features calculated by means of high resolution optical imagery, height information from a normalized digital surface model and multi-temporal medium resolution optical data. In-situ building information is given through survey data collected after the earthquake event of September 2009. Using Machine Learning techniques a work flow is presented to classify seismic building structural types. A feature selection analysis is carried out, and the features most explanatory for the determination of seismic building structural types are identified. Coping with large amounts of features and in-situ data scarcity, plausible classification results are achieved and dependencies between remotely sensed data and building stability are verified.

elib-URL des Eintrags:https://elib.dlr.de/81953/
Dokumentart:Hochschulschrift (Masterarbeit)
Titel:Estimation of seismic building structural types using remote sensing and machine learning
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Aravena Pelizari, PatrickPatrick.AravenaPelizari (at) dlr.dehttps://orcid.org/0000-0003-0984-4675159472661
Datum:2013
Referierte Publikation:Nein
Open Access:Nein
Seitenanzahl:130
Status:veröffentlicht
Stichwörter:remote sensing, machine learning, seismic building structural types, earthquake loss estimation
Institution:Ludwig-Maximilians-Universität
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)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Geiß, Christian
Hinterlegt am:17 Jul 2013 13:11
Letzte Änderung:13 Mai 2024 10:38

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