The majority of the world’s human population lives in towns and cities. High population densities mean that damage and disruption caused by natural disasters and other events has a much greater impact when urban areas are affected. Satellite remote sensing has the potential to play an important role in urban disaster monitoring and management, thanks to its relative immunity to disruption by terrestrial events. When data is required promptly and at short notice, Synthetic Aperture Radar (SAR) is of particular interest because of its ability to penetrate atmospheric conditions such as cloud and smoke.

Our capability has been to design an unsupervised tool for earthquake damage detection, based on a novel feature detection algorithm for the extraction of buildings’ signatures from SAR images. The tool and algorithm are demonstrated using SAR data from the COSMO-SkyMed constellation covering the 2009 earthquake in L’Aquila, Italy and the 2010 earthquake in Port-au-Prince, Haiti.

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