Disaster Damage Assessment of Buildings Using Adaptive Self-Similarity Descriptor

Assessment of damage caused by a disaster is significant for coordinating emergency response teams and planning emergency aid. In this letter, a robust method for rapid building damage assessment is proposed using pre- and postevent EO images and building footprints. The method uses a local self-similarity descriptor (SSD) for change detection in buildings, which is shown to be robust against variations in global illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in nonbuilding areas. Footprint is also used to differentiate small and large buildings, extract the boundary region of a building, and adapt the descriptor computation accordingly. It is shown that the adaptive SSD provides a more accurate measure of local damage on the building. The 2010 Haiti Earthquake and Typhoon Haiyan 2013 Philippines are analyzed with the proposed method, and 75/82% true positive rate and 25/15% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT and HOT.