One of the numerous fundamental tasks to perform rescue operations after the earthquake, check the status of buildings has been destroyed. The methods to obtain the damage map are two categories Shared. The first group of methods uses data before and after the earthquake, and the second group that only uses the data after the earthquakes that we want to offer a flexible and according to information that we are available to achieve the damage map. In this paper, we work on VHR satellite images of Haiti, and by UNet that is a convolution network. The learning algorithms profound changes to improve the results were intended to identify the damage of the buildings caused by the earthquake. The deep learning algorithms require very training data that it's one of the problems that we want to solve it. As well as Unlike previous studies by examining pixel by pixel degradation, ultimate precision to increase that show the success of this approach felt and has been able to reach the overall accuracy of 78.61%. The proposed method for other natural disasters such as rockets, explosions, tsunamis, and floods also destroyed buildings in urban areas is to be used.
Previous Article in event
Next Article in event
Assessment of flooding risk in Lima, Peru, through change detection based on ERS-1/2 and Sentinel-1 time seriesNext Article in session
Earthquake Damage Assessment Based on Deep Learning Change Detection Method Using VHR Images
Published: 25 November 2020 by MDPI in The 3rd International Electronic Conference on Geosciences session Mapping and Assessing Natural Disasters Using GIScience Technologies
Keywords: Damage Detecton, Deep Learning, UNet, VHR Satellite Images, Earthquake