Building damage assessment using airborne lidar

Colin Axel; Jan A. N. van Aardt

doi:10.1117/1.JRS.11.046024

18 December 2017 Building damage assessment using airborne lidar

Colin Axel, Jan A. N. van Aardt

Author Affiliations +

Funded by: U.S. Department of Transportation

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046024 (December 2017). https://doi.org/10.1117/1.JRS.11.046024

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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Colin Axel and Jan A. N. van Aardt "Building damage assessment using airborne lidar," Journal of Applied Remote Sensing 11(4), 046024 (18 December 2017). https://doi.org/10.1117/1.JRS.11.046024

Received: 5 August 2017; Accepted: 22 November 2017; Published: 18 December 2017

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