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In this case study, an approach for finding regions endangered by extreme rain is presented. The approach is based on the assumptionthat sinks in the surface are more endangered than their surroundings. The surface data, which are the source for the classification,are generated using a Cartosat stereo scene. The classification is performed by using an algorithm for retrieving the terrain positioningindex. Different classification schemes are possible, therefore a set of input parameters is iteratively computed. The classificationresults are then evaluated. For validating the classification stock data of an insurance are used. We compare the position of the reporteddamages caused by extreme rain with our classification. By doing so we got the confirmation of the assumption

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Automatic surface classification for retrieving areas which are highly endangered by extreme rain