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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. Master's, Ludwig-Maximilians-Universität.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/81953/
Document Type:Thesis (Master's)
Title:Estimation of seismic building structural types using remote sensing and machine learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Aravena Pelizari, PatrickPatrick.AravenaPelizari (at) dlr.deUNSPECIFIED
Date:2013
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:130
Status:Published
Keywords:remote sensing, machine learning, seismic building structural types, earthquake loss estimation
Institution:Ludwig-Maximilians-Universität
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)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Geiß, Christian
Deposited On:17 Jul 2013 13:11
Last Modified:12 Dec 2013 22:03

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