Advancing Wildfire Damage Assessment with Aerial Thermal Remote Sensing and AI: Applications to the 2025 Eaton and Palisades Fires

Driven by dangerous Santa Ana winds and fueled by dry vegetation, the 2025 Eaton and Palisades wildfires in California caused historic levels of devastation, ultimately becoming the second and third most destructive fires in California history. Burning at the same time and drawing from the same resources, these fires burned a combined total of 16,251 structures. The first several hours of an emerging wildfire are a crucial period for fire officials to assess potential damage and develop a timely and appropriate response. A method to quickly generate accurate estimates of structural damage is essential to providing this crucial rapid response to wildfires. In this paper, we present a machine learning approach for automated assessment of structural damage caused by wildfires. By leveraging multiple data sources in model development (satellite-based building footprints, expert-labeled post-fire damage points, fire perimeters, and aerial thermal imagery) and innovative data processing techniques, the approach can be used to identify various levels of structural damage from just aerial thermal imagery during operational use. The resulting system offers an effective approach for rapid and reliable assessment of burned structures, suitable for operational wildfire damage assessment. Results on the Eaton and Palisades Fires demonstrate the effectiveness of this method and its applicability to real-world scenarios.