CNN-based estimation of pre- and post-earthquake height models from single optical images for identification of collapsed buildings

In this paper, a novel approach is proposed to identify collapsed buildings after an earthquake using pre-event satellite image as well as post-event satellite image and airborne LiDAR data. In this regard, a convolutional neural network-based method is proposed for estimating a DHM from a single satellite image. A structure reconstruction strategy is designed to improve estimated height values and objects geometry by using the local features of shallow layers and employing a progressive context fusion method. The post-event images and their corresponding LiDAR data are used to train the proposed network. Subsequently, the trained network is employed to estimate a digital height model (DHM) from the pre-event satellite image. Finally, by investigating the difference image of pre- and post-event DHMs, collapsed buildings are identified. It is observed that the quality, kappa coefficient and overall accuracy of the obtained results are 84.86%, 91.15% and 98.78%, respectively, demonstrating a promising performance of the proposed approach.