Disaster Damage Assessment
Overview
Disaster damage assessment studies how to detect and measure harm to buildings, roads, and land after events such as earthquakes, floods, hurricanes, and wildfires. Common approaches compare pre- and post-disaster satellite or aerial images using change detection, segmentation, and building-level classifiers; newer systems also combine optical and radar data, street views, and vision-language models. Benchmarks such as xBD, RescueNet, FloodNet, and BRIGHT test tasks ranging from damage severity grading to mapping and scene understanding, while operational tools aim to support response prioritization and recovery planning. A central challenge is generalizing across unseen events, regions, and sensors: strong in-domain scores do not guarantee reliable field performance, and evaluation must account for class imbalance, uncertainty, and practical decision costs.
Latest papers
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