A SUPPORT VECTOR REGRESSION APPROACH FOR BUILDING SEISMIC VULNERABILITY ASSESSMENT AND EVALUATION FROM REMOTE SENSING AND IN-SITU DATA

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 method-ology 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 informa-tion system for seismic risk in the context of building vul-nerability assessment. In the methodology proposed, build-ing 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 build-ing vulnerability and in-situ data are available for evaluation. Index Terms — Seismic Vulnerability, Urban Area, Sup-port Vector Machine Regression, remote sensing data, in-situ data 1