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Remote Sens. 2016, 8(3), 189; doi:10.3390/rs8030189

A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds

1,2,* , 2,3,4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Author to whom correspondence should be addressed.
Academic Editors: Zhenhong Li, Roberto Tomas, Guoqing Zhou, Norman Kerle and Prasad S. Thenkabail
Received: 29 October 2015 / Revised: 3 February 2016 / Accepted: 18 February 2016 / Published: 26 February 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
View Full-Text   |   Download PDF [13002 KB, uploaded 26 February 2016]   |  


The rapid and accurate assessment of building damage states using only post-event remote sensing data is critical when performing loss estimation in earthquake emergency response. Damaged roof detection is one of the most efficient methods of assessing building damage. In particular, airborne LiDAR is often used to detect roofs damaged by earthquakes, especially for certain damage types, due to its ability to rapidly acquire accurate 3D information on individual roofs. Earthquake-induced roof damages are categorized into surface damages and structural damages based on the geometry features of the debris and the roof structure. However, recent studies have mainly focused on surface damage; little research has been conducted on structural damage. This paper presents an original 3D shape descriptor of individual roofs for detecting roofs with surface damage and roofs exhibiting structural damage by identifying spatial patterns of compact and regular contours for intact roofs, as well as jagged and irregular contours for damaged roofs. The 3D shape descriptor is extracted from building contours derived from airborne LiDAR point clouds. First, contour clusters are extracted from contours that are generated from a dense DSM of individual buildings derived from point clouds. Second, the shape chaos indexes of contour clusters are computed as the information entropy through a contour shape similarity measurement between two contours in a contour cluster. Finally, the 3D shape descriptor is calculated as the weighted sum of the shape chaos index of each contour cluster corresponding to an individual roof. Damaged roofs are detected solely using the 3D shape descriptor with the maximum entropy threshold. Experiments using post-event airborne LiDAR point clouds of the 2010 Haiti earthquake suggest that the proposed damaged roof detection technique using the proposed 3D shape descriptor can detect both roofs exhibiting surface damage and roofs exhibiting structural damage with a high accuracy. View Full-Text
Keywords: contour cluster; 3D shape descriptor; shape similarity; entropy; building damage detection contour cluster; 3D shape descriptor; shape similarity; entropy; building damage detection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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He, M.; Zhu, Q.; Du, Z.; Hu, H.; Ding, Y.; Chen, M. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds. Remote Sens. 2016, 8, 189.

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