Disaster Mapping

Disaster mapping uses various data sources, including satellite imagery and street-view images, to rapidly assess damage and aid in post-disaster response. Current research focuses on developing automated systems, employing machine learning models like convolutional neural networks and transformers, to analyze this data and improve the accuracy and speed of damage assessment, often incorporating crowdsourced information to enhance efficiency. These advancements are crucial for improving disaster response times and resource allocation, ultimately minimizing the impact of natural disasters on affected populations and infrastructure.