Building Damage Detection Using Deep Learning Models

Human life is significantly impacted by ongoing natural disasters. The global impact of the building damage they inflict is deeply significant. Deep learning can effectively analyze the extent of damage to buildings. However, the challenge is the availability of the dataset. An innovative, challenging dataset collected from satellites has been presented, encompassing the extent of structural harm caused by the earthquake in Morocco. The dataset is from a complex, practical, arid environment where the building colour is almost like the background. The dataset has been tested using U-Net, FCN, and ResUnet models. The ResUnet demonstrates superior performance metrics with pixel accuracy, recall, precision, Fl-score, kappa score, and IoU values of 96.2%, 86.4%, 86%, 86.2%, 75.7%, and 85.5%, respectively, which promotes it for practical application.