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Automated Analysis of Mobile LiDAR Data for Component‐Level Damage Assessment of Building Structures during Large Coastal Storm Events
Author(s) -
Zhou Zixiang,
Gong Jie
Publication year - 2018
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12345
Subject(s) - lidar , roof , point cloud , segmentation , component (thermodynamics) , computer science , remote sensing , environmental science , civil engineering , artificial intelligence , geology , engineering , physics , thermodynamics
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.