Disaster Damage Assessment

Disaster damage assessment aims to rapidly and accurately quantify the impact of natural disasters or conflicts on infrastructure and populations, primarily using remote sensing imagery like satellite and aerial photos. Current research focuses on developing robust and generalizable algorithms, often employing convolutional neural networks (CNNs), vision transformers, and self-supervised learning techniques, to classify damage levels and map affected areas even with limited labeled data. These advancements improve the speed and accuracy of damage assessment, enabling more efficient resource allocation for disaster response and recovery efforts, and facilitating informed decision-making for humanitarian aid and risk mitigation strategies.