Disaster assessment using computer vision and satellite imagery: Applications in detecting water-related building damages

Abstract

The increasing frequency and severity of water-related disasters such as floods, tornadoes, hurricanes, and tsunamis in low- and middle-income countries exemplify the uneven effects of global climate change. The vulnerability of high-risk societies to natural disasters has continued to increase. To develop an effective and efficient adaptation strategy, local damage assessments must be timely, exhaustive, and accurate. We propose a novel deep-learning-based solution that uses pairs of pre- and post-disaster satellite images to identify water-related disaster-affected regions. The model extracts features of pre- and post-disaster images and uses the feature difference with them to predict damage in the pair. We demonstrate that the model can successfully identify local destruction using less granular and less complex ground-truth data than those used by previous segmentation models. When tested with various water-related disasters, our detection model reported an accuracy of 85.9% in spotting areas with damaged buildings. It also achieved a reliable performance of 80.3% in out-of-domain settings. Our deep learning-based damage assessment model can help direct resources to areas most vulnerable to climate disasters, reducing their impacts while promoting adaptive capacities for climate-resilient development in the most vulnerable regions.