Near Real-Time Wildfire Damage Assessment using Aerial Thermal Imagery and Machine Learning

This project aims at developing an AI system to provide a reliable assessment of the structural damage caused by wildfires in the first burn period. Our approach uses multimodal data, including multispectral aerial images, historical post-fire damage assessment data, and building footprints, to create an association between damage data and structure footprints. We use these associations to generate features and use machine learning methods to assess the level of damage to structures. The resulting AI-driven system can be used to provide wildfire-induced structural damage assessments in near-real-time using only aerial images for future fires. We provide damage assessment results on several megafires in California to demonstrate the applicability of our approach to real wildfire scenarios.