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Gaining timely and reliable situation awareness after hazard events such as ahurricane is crucial to emergency managers and first responders. One effectiveway to achieve that goal is through damage assessment. Recently, disasterresearchers have been utilizing imagery captured through satellites or dronesto quantify the number of flooded/damaged buildings. In this paper, we proposea mixed data approach, which leverages publicly available satellite imagery andgeolocation features of the affected area to identify damaged buildings after ahurricane. The method demonstrated significant improvement from performing asimilar task using only imagery features, based on a case study of HurricaneHarvey affecting Greater Houston area in 2017. This result opens door to a widerange of possibilities to unify the advancement in computer vision algorithmssuch as convolutional neural networks and traditional methods in damageassessment, for example, using flood depth or bare-earth topology. In thiswork, a creative choice of the geolocation features was made to provide extrainformation to the imagery features, but it is up to the users to decide whichother features can be included to model the physical behavior of the events,depending on their domain knowledge and the type of disaster. The datasetcurated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features