Damage Assessment

Damage assessment research focuses on rapidly and accurately quantifying the extent and location of damage after disasters or in infrastructure inspections, primarily using image analysis. Current efforts leverage deep learning, particularly convolutional neural networks and transformers, along with techniques like contrastive learning and domain adaptation to improve efficiency and generalizability across diverse damage types and geographical locations. These advancements are crucial for efficient disaster response, infrastructure maintenance, and improved risk management, enabling faster resource allocation and more informed decision-making.