Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment

Abstract

ABSTRACTPost-disaster damage assessment is urgent for rapid save and rescue missions. Satellite and airborne imagery platforms take vertical images, in which only buildings’ roofs are observable. Unmanned aerial vehicles (UAVs) provide an alternative platform that can obtain oblique images from a scene but suffer from occlusion (hiding from view) and a smaller field of view. As imagery from each platform has several pros and cons, a fusion framework is required. A new framework is proposed in this article, based on building environment and image processing techniques to utilize the advantages of different platforms while avoiding the disadvantages. In this approach, a facade is detected in UAV oblique images by fusing a geometrical transformation and environmental information from vertical and oblique images. Fusion of roof and facade features is used to improve damage scale estimation. Damage is estimated automatically for both roof-only and facade-plus-roof images that are sourced from satellite and fused data respectively. The proposed approach is applied to several different datasets including the Haiti earthquake of 2010, Hurricane Irene of 2011, Hurricane Sandy of 2012 and the Illinois tornadoes of 2015. The proposed automatic fusion framework led to enhanced damage scale estimation in comparison with the roof-only damage assessment in these cases.