Quantitative flood assessment: Case study of floods in Germany

Abstract

In this paper, we present a quantitative analysis for a rapid mapping scenario that performs a damage assessment of the 2013 floods in Germany. The scenario is created using pre-disaster and post-disaster TerraSAR-X images and an automated annotation system. Our data set is tiled into patches and Gabor filters are used as a primitive feature method applied to each patch separately. An active learning system based on support vector machine is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our evaluation 7 categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. We show that the system supports rapid mapping scenarios (e.g., floods, tsunami, earthquake, etc.) and interactive mapping generation. In addition, with the help of this system, quantitative assessment of disasters can be carried out.