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

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 104, Issue , 2015, Pages 175-188

  Geiß, Christian ^a,b   Aravena Pelizari, Patrick ^a   Marconcini, Mattia ^a   Sengara, Wayan ^c   Edwards, Mark ^d   Lakes, Tobia ^b   Taubenböck, Hannes ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

^c BANDUNG INSTITUTE OF TECHNOLOGY   (Indonesia)

^d GEOSCIENCE AUSTRALIA   (Australia)

Author keywords

Earthquake loss estimation; Machine learning; Random Forests; Seismic building structural types; SVM; Very high and medium resolution imagery

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; EARTHQUAKES; GEOPHYSICS; LEARNING ALGORITHMS; LEARNING SYSTEMS; SEISMOLOGY; SUPPORT VECTOR MACHINES;

BUILDING STRUCTURAL; EARTHQUAKE LOSS ESTIMATION; RANDOM FORESTS; RESOLUTION IMAGERY; SVM;

REMOTE SENSING;

ACCURACY ASSESSMENT; EARTHQUAKE DAMAGE; IMAGE CLASSIFICATION; IMAGE RESOLUTION; MACHINE LEARNING; REMOTE SENSING; SATELLITE DATA; SENSOR; URBAN AREA; VULNERABILITY;

INDONESIA; PADANG; WEST SUMATRA;

EID: 84937951370     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.07.016     Document Type: Article

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