Post-earthquake assessment of building damage degree using LiDAR data and imagery

Various methods have been developed to detect and assess building's damages due to earthquakes using remotely sensed data. After the launch of the high resolution sensors such as IKONOS and QuickBird, it becomes realistic to identify damages on the scale of individual building. However the low accuracy of the results has often led to the use of visual interpretation techniques. Moreover, it is very difficult to estimate the degree of building damage (e.g. slight damage, moderate damage, or severe damage) in detail using the existing methods. Therefore, a novel approach integrating LiDAR data and high resolution optical imagery is proposed for evaluating building damage degree quantitatively. The approach consists of two steps: 3D building model reconstruction and rooftop patch-oriented 3D change detection. Firstly, a method is proposed for automatically reconstructing 3D building models with precise geometric position and fine details, using pre-earthquake LiDAR data and high resolution imagery. Secondly, focusing on each rooftop patch of the 3D building models, the pre- and post-earthquake LiDAR points belonging to the patch are collected and compared to detect whether it was destroyed or not, and then the degree of building damage can be identified based on the ratio of the destroyed rooftop patches to all rooftop patches. The novelty of the proposed approach is to detect damages on the scale of building's rooftop patch and realize quantitative estimation of building damage degree.