Natural Disaster
Natural disaster research focuses on improving preparedness, response, and recovery efforts through data analysis and advanced modeling. Current research heavily utilizes machine learning, particularly deep learning architectures like U-Net, BERT, and transformers, to analyze diverse data sources including satellite imagery, social media posts, and weather data for tasks such as damage assessment, needs identification, and misinformation detection. These advancements aim to enhance situational awareness, optimize resource allocation, and ultimately mitigate the societal impacts of natural disasters, contributing significantly to disaster management and risk reduction strategies.
Papers
Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters
Thomas Manzini, Priyankari Perali, Raisa Karnik, Mihir Godbole, Hasnat Abdullah, Robin Murphy
ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat