A new computer model produces a dynamic wildfire risk map, starting with the state of California
Embargoed for release until December 9, 2025
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Washington, D.C., December 9, 2025 – Wildfires pose a significant threat across the southwestern United States, due to the region’s unique topography and weather conditions. Accurately identifying locations at the highest risk of a severe wildfire is critical for implementing preventive measures.
With this goal in mind, scientists from the University at Buffalo have developed GeoFlame VISION, a proposed computer model that uses AI and satellite imagery to produce a dynamic wildfire risk map at a granular spatiotemporal scale for the entire United States. The authors will present a case study of California using their model on Dec. 9 at the Society for Risk Analysis Annual Meeting in Washington, D.C.
“This novel approach of integrating remote sensing data with machine learning and AI will not only help with efficient wildfire mitigation, but also aid in decision-making related to land management and the controlled expansion of Wildland-Urban-Interface (WUI) regions – which in turn can lower the risk of wildfire-induced damages to the critical infrastructures and WUI communities in the future”, says Sayanti Mukherjee, assistant professor of industrial and systems engineering at the University at Buffalo and corresponding author of the study.
Preliminary findings from the dynamic wildfire risk map of California:
“The wildfire risk in a region not only depends on the topographical, landcover, and weather-related variables, but also on the built environment, such as the buildings and power grid infrastructure, which is often overlooked in the traditional physics-based wildfire spread models,” says Poulomee Roy, lead author of the study and a doctoral candidate at the University at Buffalo. “Thus, the interactions among all these factors — which we include in our study — are instrumental in modeling the dynamic wildfire risk.”
To create the map, the researchers used satellite imagery data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) to extract information on historical burned areas from 2015 to 2022 at a weekly time scale (with small areas marked burned or unburned). This dataset was integrated with information on variables such as topography, elevation, climate, vegetation, windspeed, and the locations of critical infrastructure, including residential buildings and power stations. Using advanced vision-based AI and other technologies, a pixel-based predictive analysis of the wildfire-burnt areas was then performed. The map is about 92% accurate at predicting dynamic wildfire risk at a granular spatiotemporal scale (based on real data from past wildfires).
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EDITORS NOTE:
This research will be presented on December 10 at 10:30 EST at the Society for Risk Analysis (SRA) Annual Conference at the Downtown Westin Hotel in Washington, D.C. SRA Annual Conference welcomes press attendance. Please contact Emma Scott at emma@bigvoicecomm.com to register.
About Society for Risk Analysis
The Society for Risk Analysis (SRA) is a multidisciplinary, global organization dedicated to advancing the science and practice of risk analysis. Founded in 1980, SRA brings together researchers, practitioners, and policymakers from diverse fields including engineering, public health, environmental science, economics, and decision theory. The Society fosters collaboration and communication on risk assessment, management, and communication to inform decision-making and protect public well-being. SRA supports a wide range of scholarly activities, publications, and conferences. Learn more at sra.org.
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