Fast and Efficient Evaluation of Building Damage From Very High Resolution Optical Satellite Images

In this paper, we present a novel combination of object features to both match buildings from predisaster images to shapes in a postdisaster image and assess damage on those buildings. These features include scale profile ratios extracted from a tree of shapes representation of the original image as well as texture features. A supervised classifier is used to classify building damage into three representative classes tied to the European Macroseismic Scale (EMS-98). The method is compared to visual inspection results as well as other automated methods. Results clearly show the benefits of our method for fast crisis mapping applications with few human inputs required.