Residential Wildfire Structural Damage Detection Using Deep Learning to Analyze Uncrewed Aerial System (UAS) Imagery

Wildfire damage related to residential areas causes enormous economic and social losses. Therefore, appropriate measures must be taken at the appropriate time to reduce the damage to residential infrastructure caused by the disaster. Post-wildfire assessment of a home can play an important role in understanding the overall extent of damage and developing a disaster mitigation plan for the future. Unfortunately, these assessments require extensive on-site investigation, which takes a lot of human resources and time. As a way to compensate for this, this study provides an efficient and effective methodology to perform damage assessments of housing after a wildfire using deep learning techniques to efficiently analyze unmanned aircraft systems' (UAS) imagery of residential areas. Application of this efficient methodology reduces the need for on-site investigation and associated safety risks as well as enables decision-makers to rapidly assess a large area to determine the relative degree of damage to structures and develop cost estimates for a community for damages after a wildfire.