Building Damage Extraction from Post-earthquake Airborne LiDAR Data

作     者：DOU Aixia MA Zongjin HUANG Shusong WANG Xiaoqing 

作者机构：Institute of GeologyChina Earthquake Administration Institute of Earthquake ScienceChina Earthquake Administration 

出 版 物：《Acta Geologica Sinica(English Edition)》 (地质学报（英文版）)

年 卷 期：2016年第90卷第4期

页      面：1481-1489页

核心收录：

学科分类：070801[理学-固体地球物理学] 0709[理学-地质学] 0819[工学-矿业工程] 07[理学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 0816[工学-测绘科学与技术] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.41404046) the World Bank GFDRR group for providing financial support to acquire the data 

主　　题：airborne LiDAR surface normal building damage earthquake damage extraction 

摘      要：Building collapse is a significant cause of earthquake-related casualties; therefore, the rapid assessment of buildings damage is important for emergency management and rescue. Airborne light detection and ranging （LiDAR） can acquire point cloud data in combination with height values, which in turn provides detailed information on building damage. However, the most previous approaches have used optical images and LiDAR data, or pre- and post-earthquake LiDAR data, to derive building damage information. This study applied surface normal algorithms to extract the degree of building damage. In this method, the angle between the surface normal and zenith （0） is used to identify damaged parts of a building, while the ratio of the standard deviation to the mean absolute deviation （σ/δ） of θ is used to obtain the degree of building damage. Quantitative analysis of 85 individual buildings with different roof types （i.e., flat top or pitched roofs） was conducted, and the results confirm that post-earthquake single LiDAR data are not affected by roof shape. Furthermore, the results confirm that θ is correlated to building damage, and that σ/δ represents an effective index to identify the degree of building damage.