Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR

doi:10.11947/j.AGCS.2015.20130785

Abstract

Abstract:

Detection of the damaged building is the obligatory step prior to evaluate earthquake casualty and economic losses. It's very difficult to detect damaged buildings accurately based on the assumption that intact roofs appear in laser data as large planar segments whereas collapsed roofs are characterized by many small segments. This paper presents a contour cluster shape similarity analysis algorithm for reliable building damage detection from the post-earthquake airborne LiDAR point cloud. First we evaluate the entropies of shape similarities between all the combinations of two contour lines within a building cluster, which quantitatively describe the shape diversity. Then the maximum entropy model is employed to divide all the clusters into intact and damaged classes. The tests on the LiDAR data at El Mayor-Cucapah earthquake rupture prove the accuracy and reliability of the proposed method.

Key words: contour cluster, shape similarity, entropy, damaged building detection, post-earthquake airborne LiDAR point cloud

CLC Number:

P237

HE Meizhang, ZHU Qing, DU Zhiqiang, ZHANG Yeting, HU Han, LIN Yueguan, QI Hua. Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(4): 407-413.

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