Post-tornado automated building damage evaluation and recovery prediction by integrating remote sensing, deep learning, and restoration models

Abstract

This study introduces a novel methodology that integrates remote sensing, deep learning, and restoration models to streamline building damage assessment and recovery time predictions following tornado events. In contrast to existing research primarily focused on pre-hazard mitigation and preparedness, this study advances the field by extending the application of engineering models to the post-hazard emergency response and recovery phase. The novelty lies in utilizing remote sensing and deep learning to automate the generation of large-scale maps for tornado damage. Then, building damage evaluation is integrated with restoration models for rapid estimations of post-disaster restoration time and cost. Through a comprehensive application study focused on the 2011 Joplin Tornado, the methodology is demonstrated to be fully automated. The predictions were validated against historical reports, highlighting the methodology's effectiveness in generating accurate damage evaluation and restoration predictions. This study stands out as the first to leverage remote sensing imagery-based damage evaluation to extend the utility of regional risk assessment beyond pre-tornado planning, thus enhancing post-tornado disaster response and recovery efforts.