This paper quantitatively evaluates the suitability of multi-sensor remote sensing to assess the seismic vulnerability of buildings for the example city of Padang, Indonesia. Features are derived from remote sensing data to characterize the urban environment and are subsequently combined with in situ observations. Machine learning approaches are deployed in a sequential way to identify meaningful sets of features that are suitable to predict seismic vulnerability levels of buildings. When assessing the vulnerability level according to a scoring method, the overall mean absolute percentage error is 10.6%, if using a supervised support vector regression approach.
Geis, C., Lakes, T., Post, J., Taubenbock, H., Tisch, A., Tyagunov, S. Assessment of Seismic Building Vulnerability from Space,
Earthquake Enginering Reserch, 2014, с. 1553-1583..
Geis, C., Lakes, T., Post, J., Taubenbock, H., Tisch, A., Tyagunov, S. .
Assessment of Seismic Building Vulnerability from Space.
: Earthquake Enginering Reserch, 2014, с. 1553-1583..
Geis, C., Lakes, T., Post, J., Taubenbock, H., Tisch, A., Tyagunov, S. (2014)
Assessment of Seismic Building Vulnerability from Space,
: Earthquake Enginering Reserch, с. 1553-1583.
Geis, C.,
Lakes, T.,
Post, J.,
Taubenbock, H.,
Tisch, A., &
Tyagunov, S.
(2014).
Assessment of Seismic Building Vulnerability from Space. Earthquake spectra. Earthquake Enginering Reserch, 30 (4), с. 1553-1583..
