Global Disaster

Global disaster research focuses on understanding and mitigating the risks of large-scale events, encompassing natural hazards and human-induced crises. Current efforts leverage machine learning, particularly deep learning models like ResNet-50 and algorithms such as logistic regression and label spreading, to analyze diverse data sources including remote sensing imagery, social media, and weather statistics for improved risk assessment and prediction, particularly for floods, landslides, and earthquakes. These advancements aim to enhance disaster preparedness and response, leading to more effective resource allocation and potentially reducing human suffering and economic losses. The integration of diverse data modalities and advanced modeling techniques is improving the timeliness and accuracy of disaster prediction and impact assessment.