Integrated framework for hurricane-induced debris detection and prediction: enhancing coastal resilience

Timely and accurate detection of hurricane-induced debris is critical for effective disaster response and enhanced community resilience.While post-disaster aerial imagery is widely available, robust debris segmentation methods applicable across diverse disaster regions remain limited.Developing a generalized solution is challenging due to variations in environmental and imaging conditions that alter the visual signatures of debris, further complicated by the scarcity of high-quality labeled training data.This study addresses these challenges by fine-tuning pre-trained foundational vision models, achieving robust performance despite using a relatively small yet high-quality dataset.Specifically, this work introduces an open-source dataset comprising approximately 1,200 manually annotated aerial RGB images collected after Hurricanes Ian, Ida, and Ike.To mitigate human biases and enhance labeling quality, annotations from multiple experts are strategically aggregated, complemented by visual prompt engineering.The resulting fine-tuned model, named fCLIPSeg, achieves a Dice score of 0.70 on imagery from Hurricane Ida-a disaster event completely excluded from training-with virtually no false positives in debris-free regions.This research provides the first eventagnostic debris segmentation model deployable with standard RGB imagery, making it particularly well-suited for rapid, large-scale post-disaster impact assessments and recovery planning.