PRESSNet: Assessment of Building Damage Caused by the Earthquake

of life and property often occur due to natural disasters and other significant occurrences like earthquakes, which make manual damage assessment a time-consuming and inefficient process. In an attempt to address this challenge, researchers have been investigating the field of automated damage assessment in Remote Sensing. With time, this area of research has transformed from conventional machine learning techniques to more sophisticated deep learning techniques. The study puts forward the PRESSNet model as a solution for assessing building damage. The effectiveness of the proposed PRESSNet model is compared to that of a baseline model, PSPNet, and ResNet 50, across different types of damage. This study contributes by introducing the spatial attention module to the baseline model. The xBD Dataset was used both before and after the Palu earthquake disaster. The results show that PRESSNet performs similarly or slightly better than the baseline model in all damage categories. This illustrates the impressive ability of the proposed PRESSNet architecture to accurately detect and classify building damage. This research sheds light on the development of effective models for assessing disaster damage and lays the foundation for future progress in this crucial area.