Building damage assessment using airborne lidar

Jan van Aardt,Colin Axel

doi:10.1117/1.jrs.11.046024

Jan van Aardt, Colin Axel

Open Access

https://doi.org/10.1117/1.jrs.11.046024

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Abstract

The assessment of building damage following a natural disaster is a crucial step in determining the impact of the event itself and gauging reconstruction needs. Automatic methods for deriving damage maps from remotely sensed data are preferred, since they are regarded as being rapid and objective. We propose an algorithm for performing unsupervised building segmentation and damage assessment using airborne light detection and ranging (lidar) data. Local surface properties, including normal vectors and curvature, were used along with region growing to segment individual buildings in lidar point clouds. Damaged building candidates were identified based on rooftop inclination angle, and then damage was assessed using planarity and point height metrics. Validation of the building segmentation and damage assessment techniques were performed using airborne lidar data collected after the Haiti earthquake of 2010. Building segmentation and damage assessment accuracies of 93.8% and 78.9%, respectively, were obtained using lidar point clouds and expert damage assessments of 1953 buildings in heavily damaged regions. We believe this research presents an indication of the utility of airborne lidar remote sensing for increasing the efficiency and speed at which emergency response operations are performed.

Highlights

Building damage assessment represents an urgent response priority following a natural disaster
The following paragraphs take a closer look at the sources of false positives and negatives during building segmentation
We proposed and evaluated an automated technique for assessing building damage via airborne lidar point clouds

Summary

Introduction

Building damage assessment represents an urgent response priority following a natural disaster. This is true because determination of the damage status of buildings allows first responders to be directed to the most important locations, while resources, which are often a limiting factor in emergency response, can be utilized to their full potential. Methods using SAR imagery typically exploit backscattering intensity and phase information to locate damage.[3] Success with SAR data in urban areas has been limited due to issues arising from an oblique viewing geometry, occlusions, and multiple scattering from tall buildings.[2] Some building types are undetectable in the 2-D domain due to a lack of height information, such as “pancake collapses,” in which one or more stories collapse onto themselves because of structural failures.[4]

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