A support vector regression approach for building seismic vulnerability assessment and evaluation from remote sensing and in-situ data

Panagiota Matsuka, Jocelyn Chanussot, Erwan Pathier, Philippe Guéguen

Abstract: In this paper, seismic vulnerability assessment is addressed under the umbrella of remote sensing. A study for estimating and evaluating information for assessing seismic vulnerability based on a building basis is presented. The proposed methodology utilizes the capabilities of remote sensing and combines in-situ data tested in the area of Grenoble (France). A map is estimated in agreement with in-situ data, as support information system for seismic risk in the context of building vulnerability assessment. In the methodology proposed, building attributes such as roof identification, building height and characteristic scale are extracted from very high resolution panchromatic data, and an accurate digital elevation model. Support vector machine regression is used to estimate building vulnerability and in-situ data are available for evaluation.