A framework for automated assessment of post-earthquake building damage using geospatial data

Abstract

Based on the triangulated irregular network (TIN) model, barycentric coordinates and random points, a new method was developed for more accurate characterization of 3D shape signatures of buildings using Light Detection and Ranging (LiDAR) data and Geographic Information System (GIS). The new method was applied to four simulated building models with flat, pent, gable and hip roofs to test the detection of changes in 3D building shapes in a post-earthquake scenario: (1) a three-storey building model becomes two-storey after losing the first floor; (2) a severe damage of a two-storey building model; and (3) a total collapse of a two-storey building model. The new method was then applied to real LiDAR data from four buildings in Harris County, TX, USA. All the changes in 3D shapes of the models and real buildings were successfully detected using 3D shape signatures. Sensitivity analyses were carried out to test the influence of LiDAR point density and new points in TIN triangles on 3D shape signatures. The results suggest that LiDAR point density of 0.5 point m−2 or higher can generate stable 3D shape signatures of buildings, and that the density of the new points in TIN triangles does not affect 3D shape signatures significantly. Built upon the new method and results, a framework was proposed for an automated assessment of post-earthquake building damage using geospatial data. A flowchart was presented to provide more details of the framework, and the advantages and limitations of the framework were also discussed. It is expected that the framework can be effectively applied to post-earthquake building damage assessment and other disaster scenarios that involve major changes in 3D building shapes.