Automated building damage classification for the case of the 2010 Haiti earthquake

Building damage evaluation is an important part of disaster response and recovery phases of the emergency management cycle. Earth observation data acquired by remote sensing satellites can provide useful information for damage mapping. Unfortunately, very high spatial resolution images are quite large and require analysis by multiple expert photo-interpreters in order to extract meaningful information in short time. This can lead to human errors caused by fatigue and varying degrees of image understanding from one analyst to the next. In this paper, we propose an object-based supervised learning scheme using geometric, scale and textural object features to detect the level of damage on buildings affected by an earthquake. The method is applied to the case of the 2010 Haiti earthquake.