Training and Assessment of a Damage Classification Deep Learning Model for the 2025 Palisades Fires in Southern California

Abstract

Providing preliminary damage reports is essential to residents of post-disaster zones who need this information while planning their return to their property. As fire size, severity and frequency increase, it may become harder for local authorities to assess the amount of damage caused by these fires in a timely manner. High resolution satellite imagery of the 2025 Palisades Fire’s post disaster zone was used to train a deep learning model in ArcGIS Pro that classifies building footprint as damaged or undamaged. The model performed with high scores on several accuracy metrics, showing that off the shelf deep learning models can be applied to new data and trained to near perfect agreement, even on less powerful computers. With deep learning tools becoming more accessible, it may be wise to incorporate them as part of post disaster measures to maintain the public informed with real-time and accurate information. However, while these tools can be used alongside other demographic data to form relevant and informative damage reports, they suffer from accessibility issues like high imagery prices, high computing requirements, and expensive licensing that could make it difficult to apply this emerging technology in a broad range of scenarios.