Object-based Detection of Destroyed Buildings Based on Remotely Sensed Data and GIS

The paper describes an object-based method to detect destroyed buildings as a consequence of an earthquake. The investigation is based on the analysis of remotely sensed raster and vector-based data. The methodology includes three main steps: generation of features defining the states of buildings, classification of building state and data import in GIS. This paper concentrates on the first step of the three, the generation of features. The appropriately selected features are indispensable for the following successful classification. The described methodology is applied to remotely sensed images of areas that had been subject to an earthquake. Our preliminary results confirm the potential of the proposed approach for detection of the building state. The change detection methodology has been developed solely with Open Source Software. GRASS GIS is involved for vector and raster data processing and presentation. Programming languages Python and Bash are used to develop new GRASS-modules.