Towards a rapid automatic detection of building damage using remote sensing for disaster management

Abstract

Purpose– Satellite and airborne images are increasingly used at different stages of disaster management, especially in the detection of infrastructure damage. Although semi- or full automatic techniques to detect damage have been proposed, they have not been used in emergency situations. Damage maps produced by international organisations are still based on visual interpretation of images, which is time- and labour-consuming. The purpose of this paper is to investigate how an automatic mapping of damage can be helpful for a first and rapid assessment of building damage.Design/methodology/approach– The study area is located in Port-au-Prince (Haiti) stricken by an earthquake in January 2010. To detect building damage, the paper uses optical images (15 cm of spatial resolution) coupled with height data (LiDAR, 1 m of spatial resolution). By undertaking an automatic object-oriented classification, the paper identifies three categories of building damages: intact buildings, collapsed buildings and debris.Findings– Data processing for the study area covering 11 km2 took about 15 hours. The accuracy of the classification varies from 70 to 79 per cent depending to the methods of assessment. Causes of errors are numerous: limited spectral information of the optical images, resolution difference between the two data, high density of buildings but most importantly, certain types of building collapses could not be detected by vertically taken images (the case of data in this study).Originality/value– The automatic damage mapping developed in this paper proves to be reliable and could be used in emergency situations. It could also be combined with manual visual interpretation to accelerate the planning of humanitarian rescues and reconstruction.