By combining satellite imagery and artificial intelligence, their model offers a potential breakthrough in wildfire management and emergency response.

Detailed in an early study proof published in Artificial Intelligence for the Earth Systems, the USC model uses satellite data to track a wildfire's progression in real time, then feeds this information into a sophisticated computer algorithm that can accurately forecast the fire's likely path, intensity and growth rate.

Above : DEREK VINCENT MALLIA, Department of Atmospheric Sciences.

The study comes as California and much of the western United States continues to grapple with an increasingly severe wildfire season. Multiple blazes, fueled by a dangerous combination of wind, drought and extreme heat, are raging across the state. Among them, the Lake Fire, the largest wildfire in the state this year, has already scorched over 38,000 acres in Santa Barbara County.

Reverse-engineering wildfire behavior with AI

The researchers began by gathering historical wildfire data from high-resolution satellite images. By carefully studying the behavior of past wildfires, the researchers were able to track how each fire started, spread and was eventually contained. Their comprehensive analysis revealed patterns influenced by different factors like weather, fuel (for example, trees, brush, etc.) and terrain.

They then trained a generative AI-powered computer model known as a conditional Wasserstein Generative Adversarial Network, or cWGAN, to simulate how these factors influence how wildfires evolve over time. They taught the model to recognize patterns in the satellite images that match up with how wildfires spread in their model.

They then tested the cWGAN model on real wildfires that occurred in California between 2020 and 2022 to see how well it predicted where the fire would spread.