Automated Analysis of Mobile LiDAR Data for Component-Level Damage Assessment of Building Structures during Large Coastal Storm Events

Zixiang Zhou, Jie Gong

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)373-392
Number of pages20
JournalComputer-Aided Civil and Infrastructure Engineering
Volume33
Issue number5
DOIs
StatePublished - May 2018

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Automated Analysis of Mobile LiDAR Data for Component-Level Damage Assessment of Building Structures during Large Coastal Storm Events'. Together they form a unique fingerprint.

Cite this