EMYNet-BDD: EfficientViTB Meets Yolov8 in the Encoder Decoder Architecture for Building Damage Detection Using Postevent Remote Sensing Images

Natural disasters commonly occur in all regions around the world and cause huge financial and human losses. One of the main effects of earthquakes and floods is the destruction of buildings. Photogrammetric and remote sensing (RS) data track changes and detect damages in these events. Considering the evolution in deep learning (DL) techniques, the possibility of accurate information extraction from the RS-based data is increased. DL methods effectively show the damaged regions for decision making and immediate actions for crisis management. The present study is based only on postevent RS images, which apply an encoder-decoder network composed of pretrained Efficient ViTB and Yolov8 network blocks as encoder path and the modified-Unet blocks as decoder path for building damage detection (BDD). Compared with methods that use only one network in their encoder path, the presented method achieves better results. To investigate the performance of the proposed method, three datasets affected by different natural disasters are considered. The first dataset is the satellite images of the 2023 Turkey earthquake, the second dataset is associated with the satellite images of the 2023 Morocco earthquake, and the third dataset contains the satellite images of the 2023 Libya flood. The proposed method ultimately reaches the overall accuracy of 97.62%, 98.63%, and 96.43% and the kappa coefficient of 0.86, 0.85, and 0.84 for the first, second, and third dataset, respectively, which shows the proper performance of the proposed method for BDD.