TY - JOUR
T1 - Automated Analysis of Mobile LiDAR Data for Component-Level Damage Assessment of Building Structures during Large Coastal Storm Events
AU - Zhou, Zixiang
AU - Gong, Jie
N1 - Publisher Copyright: © 2018 Computer-Aided Civil and Infrastructure Engineering Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5
Y1 - 2018/5
N2 - Rapid assessment of building damages due to natural disasters is a critical element in disaster management. Although airborne-based remote sensing techniques have been successfully applied in many postdisaster scenarios, automated building component-level damage assessment with terrestrial/mobile LiDAR data is still challenging to achieve due to lack of reliable segmentation methods for damaged buildings. In this research, a novel building segmentation and damage detection approach is proposed to realize automated component-level damage assessment for major building envelop elements including wall, roof, balcony, column, and handrail. The proposed approach first conducts semantic segmentation of building point cloud data using a rule-based approach. The detected building components are then evaluated to determine if the components are damaged. The authors applied this method on a mobile LiDAR data set collected after Hurricane Sandy. The results demonstrate that the approach is capable of achieving 96% and 86% parsing accuracy for wall façades and roof facets, and obtain 82% and 78% of detection accuracy for damaged walls and roof facets.
AB - Rapid assessment of building damages due to natural disasters is a critical element in disaster management. Although airborne-based remote sensing techniques have been successfully applied in many postdisaster scenarios, automated building component-level damage assessment with terrestrial/mobile LiDAR data is still challenging to achieve due to lack of reliable segmentation methods for damaged buildings. In this research, a novel building segmentation and damage detection approach is proposed to realize automated component-level damage assessment for major building envelop elements including wall, roof, balcony, column, and handrail. The proposed approach first conducts semantic segmentation of building point cloud data using a rule-based approach. The detected building components are then evaluated to determine if the components are damaged. The authors applied this method on a mobile LiDAR data set collected after Hurricane Sandy. The results demonstrate that the approach is capable of achieving 96% and 86% parsing accuracy for wall façades and roof facets, and obtain 82% and 78% of detection accuracy for damaged walls and roof facets.
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U2 - https://doi.org/10.1111/mice.12345
DO - https://doi.org/10.1111/mice.12345
M3 - Article
VL - 33
SP - 373
EP - 392
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
SN - 1093-9687
IS - 5
ER -