Building Damage Detection Method Based on Normal Vector Computation of Airborne Point Cloud Data

Earthquake is one of the major natural disaster which endangers the existence and development of mankind. The collapse of buildings caused by earthquake is one of the important reasons causing the casualties. Therefore, it is very important to recognize and extract the damage of buildings quickly. Airborne LiDAR can acquire high resolution point cloud data, which can provide height or other information for building damage detection. This paper finds an effective factor which can be used to detect building damage when we only have post-earthquake point cloud data. The factor is an angle, which is calculated from the normal vector and zenith of point cloud data. Through the test of large scale of point cloud data, the Kappa coefficient is 0.788, which proves the validity of this method. The result provides new ideas for the detection of building damage type and damage degree.