Accelerating Tornado Disaster Response with Automated Level of Damage Classification: Dataset and Benchmark

Tornadoes are among the most destructive natural disasters in the United States, with over one thousand occurrences annually, causing significant human and economic losses. Rapid and accurate damage assessment is critical for effective disaster response, but traditional manual methods, relying on the Enhanced Fujita (EF) scale, are time-consuming and prone to human error. This study addresses these challenges by developing an automated tornado damage classification system using deep learning. Leveraging a curated dataset of thousands of labeled post-event images from NOAA’s Storm Damage Viewer, categorized by EF0 to EF5 ratings, this work trained and evaluated deep learning models to predict damage severity. The results demonstrate the potential for scalable and accurate damage assessments, offering critical insights for emergency responders. Additionally, the study introduces a benchmark tornado damage dataset to advance future research in this domain. These contributions aim to enhance disaster response efficiency and resource allocation in tornado-affected areas.