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Seismic vulnerability assessment using Support Vector Machine classification for remote sensing and in-situ Data

A new framework for evaluating seismic vulnerability classes at urban scale on a building basis is proposed in this paper. The proposed methodology utilizes the capabilities of remote sensing techniques combined with information from in-situ evaluation by expert of a small percentage of buildings. The end scope is an estimated vulnerability map classifying all the buildings according to the EMS98 vulnerability classes, providing initial information to decision support system for seismic risk in the context of vulnerability assessment. We tested the methodology in the city of Grenoble (France), using only 2 building attributes from remote sensing: roof type and building height (extracted from a very high resolution image, and an 1-m-resolution digital elevation model). Results show that with only these 2 attributes it is difficult to classify the building in the 4 existing classes. However using two classes (merging EMS98 classes A and B and classes C and D) give satisfactory results.