This post is also available in:
עברית (Hebrew)
California (as well as much of the western US) is dealing with an ever-increasing problem of wildfires fueled by a dangerous combination of wind, drought, and extreme heat.
To deal with this danger, researchers at the University of Southern California found a new method to combine satellite imagery and AI, offering a breakthrough in wildfire management and emergency response – the model tracks the wildfire’s progression in real time using satellite data, then feeds the information into an algorithm that accurately forecasts its likely path, intensity, and growth rate.
According to Techxplore, the researchers began by gathering historical wildfire data from high-resolution satellite images, studying the behavior of past wildfires, and tracking how each fire started, spread, and was eventually contained. This analysis revealed patterns influenced by factors like weather, fuel, and terrain. They then trained an AI model called cWGAN to simulate how these factors influence how wildfires evolve over time.
The research team then taught the model to recognize patterns in the satellite images that match up with how wildfires spread in their model, following up by testing it on real wildfires that occurred in California between 2020 and 2022 to see how well it predicted where the fire would spread.
“This model represents an important step forward in our ability to combat wildfires,” said Bryan Shaddy, the study’s corresponding author. “By offering more precise and timely data, our tool strengthens the efforts of firefighters and evacuation teams battling wildfires on the front lines.”
Study co-author Assad Oberai spoke about the project: “Wildfires involve intricate processes: Fuel like grass, shrubs or trees ignites, leading to complex chemical reactions that generate heat and wind currents. Factors such as topography and weather also influence fire behavior—fires don’t spread much in moist conditions but can move rapidly in dry conditions…These are highly complex, chaotic and nonlinear processes. To model them accurately, you need to account for all these different factors. You need advanced computing.”
