Building damage feature analyses based on post-earthquake airborne LiDAR data

Building damage detection can be more accuracy because that the airborne LiDAR system can acquire height of buildings and other high resolution information, therefore airborne LiDAR data will be an important data source in post-earthquake disaster evaluation in the future. This paper chooses the typical building point cloud data on different damage condition from the airborne LiDAR point cloud data acquired after the M_W7.0 earthquake in Haiti in 2010, and compares the distribution of the features such as height, slope and normal vector of damaged and non-damaged buildings. And then we establish the building damage determination factors, such as mean height deviation, slope value of building roof, and the intersection angle between normal vector and zenith direction. The results show that all factors can be used to recognize building damage in different condition, that is to say, mean height deviation can be used to detect the damage of single building, the slope value can be used to detect the damage part border of building, the intersection angle is a better factor that can be used to detect building damage in large areas.