Earthquake Damage Assessment Based on Deep Learning Method Using VHR Images

Abstract

One of the numerous fundamental tasks to perform rescue operations after an earthquake is to check the status of buildings that have been destroyed. The methods to obtain the damage map are in two categories. The first group of methods uses data before and after the earthquake, and the second group only uses the data after the earthquakes that we want, to offer a flexible damage map according to information that we are available to achieve. In this paper, we work on VHR satellite images of Haiti and UNet which is a convolution network. The learning algorithm’s profound changes to improve the results were intended to identify the damage of the buildings caused by the earthquake. The deep learning algorithms require training data and that is one of the problems that we want to solve. As well as previous studies examining pixel by pixel degradation, ultimate precision to increase that shows the success of this approach felt and has been able to reach the overall accuracy of 68.71%. The proposed method for other natural disasters such as rockets, explosions, tsunamis, and floods also destroyed buildings in urban areas is to be used.