Abstract
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.
Highlights
Building collapse is one of the primary causes of heavy human casualties in destructive earthquakes [1]
This study proposes a method of damaged roof detection for both roofs suffering surface damage and roofs suffering structural damage based on a 3D shape descriptor using post-earthquake airborne
We employ the normalized Fourier descriptor to measure the approximate shape similarity between two contours based on the assumption that the shape differences among the intact contour clusters are only produced by geometric translations and scaling, whereas the damaged contour clusters do not follow this assumption
Summary
Building collapse is one of the primary causes of heavy human casualties in destructive earthquakes [1]. Airborne laser scanning systems are suitable for damaged roof detection because precise 3D point clouds can be rapidly obtained at all times and under most weather conditions without entering the quake-stricken area [19,20], and the elevation accuracy is higher compared to point clouds derived from vertical optical or SAR imagery [18,21] Change detection using both pre- and post-event remote sensing data is a popular method of acquiring building damage information because detailed pre-event data are invaluable in reconnaissance [18,22,23,24]. We do not provide an exhaustive review of all these methods; instead, we highlight only the 3D-feature-based approaches using only post-event airborne point clouds that are directly relevant to our work in the subsection
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