Damaged buildings classification using residual network

Abstract

Classification of damaged buildings post hurricanes could be a cinch with the implementation of deep learning networks. Many researchers have engaged in conducting such tasks with the help of neural networks on satellite images of posthurricane buildings, while few of them grant appropriate attention to the data preprocessing methods even considering some unique properties of the satellite images. In this paper, we investigate several traditional data preprocessing approaches and some novel image preprocessing methods while making comparisons and analyses. Eventually, we could reach an accuracy over 98% with appropriate preprocessing approaches with a simplified ResNet network. Furthermore, we also analyze the effectiveness of the data augmentation method for damaged building classification, including geometric transformation, photometric transformation, and image mixing.