Significant enhancements deliver critical fire intelligence in the wildland-urban interface, helping utilities and emergency agencies protect lives and infrastructure
LA JOLLA, Calif., April 22, 2026 /PRNewswire/ — Technosylva, the global leader in wildfire and extreme weather science and technology, today launched major enhancements to its urban conflagration model that predicts how fires spread through populated areas and quantifies risk to buildings. The model addresses a key limitation of traditional wildfire science: much of it has focused on wildland areas, classifying urban areas as “non-burnable.” This limitation slows fire simulations at the community boundary, leaving fire agencies, utilities, and insurers with limited forward visibility into how fire will behave in populated communities.
Technosylva’s capabilities provide two notable wildfire modeling enhancements. First, the urban conflagration model simulates how fires will behave in the wildland urban interface (WUI), where characteristics such as structure density, vegetation encroachment, and fuel types result in fundamental differences compared to wildland fires. Second, the Dynamic Building Loss Factor provides unprecedented insight into the vulnerability of structures. This information enables utilities and agencies to undertake appropriate mitigations, such as asset hardening, undergrounding lines, vegetation management, and community education and engagement.
“Recent devastating fires have made one thing clear: populated areas face disproportionate impacts—and require greater focus to protect them,” said Bryan Spear, CEO of Technosylva. “Traditional wildfire models were designed for wildland fuels and fire behavior. Our approach builds on that foundation by showing how fires actually move through communities. By more accurately modeling the risks and consequences, utilities and fire agencies can make smarter, risk-based decisions to mitigate wildfire risks, communicate threats, maintain power, and better protect the communities they serve.”
According to a 2023 article in the Proceedings of the National Academy of Sciences [1], “community fire destruction has become a national crisis.” Recent disasters in Lahaina, Gatlinburg, and Marshall show why. Many communities aren’t built to withstand ignition, and once a structure catches fire, it can quickly spread flames and embers to neighboring buildings. The result is fast-moving, large-scale destruction with lasting impacts on entire communities.
Key Technology Advances Addressing Critical Industry Needs
Technosylva’s unique model was trained on a comprehensive database of WUI fires, examining environmental conditions, weather patterns, and fuel characteristics to understand the drivers of urban conflagration. One of the primary challenges in modeling fire behavior in the built environment is a limited number of historical fires upon which to draw conclusions and build scalable models. Technosylva’s modeling approach has overcome these challenges, effectively capturing the complex interactions between wildfire and the built environment.
Notable enhancements to Technosylva’s modeling approach include:
WUI Fuel Mapping: Development of 12 unique WUI fuel types that more accurately reflect the manner in which the infrastructure in the built environment becomes a fuel source for the fire. This is critical for understanding how the characteristics of the built environment impact the rate of spread, intensity, and speed of fires in the WUI.Dynamic Building Loss Factor: Machine learning models to capture expected building loss, leveraging characteristics such as structure characteristics and building age that drive vulnerability. Combined with assessments of topography, vegetation, and other building properties such as density and proximity to roads, this intelligence identifies not just whether a community is threatened, but the types of structures and conditions that result in the highest risk.Characterization of Fire Behavior Under Extreme Conditions: Calibrated to accurately reflect urban encroachment and fire spread rates in WUI environments—particularly during the most extreme events. Capturing fires that have historically been labeled as “outliers” is critical for utilities and communities to understand and prepare for potential worst-case scenarios.High-Resolution Weather Integration: Captures localized wind patterns, humidity gradients, and temperature variations at a scale matched to “neighborhood-level” fire behavior.
Large-scale urban fires were once rare, but in recent years their frequency and severity has increased dramatically. When wildfires reach communities, the “fuel” is no longer just vegetation—it’s homes and businesses. In Lahaina alone, a single urban conflagration caused an estimated $4 to $6 billion in economic losses. The consequences can be devastating for both life and property. Technosylva’s modeling has evolved to capture how fires spread through the built environment, enabling utilities and agencies to make more informed, risk-based decisions.
[1] https://www.fs.usda.gov/rm/pubs_journals/2023/rmrs_2023_calkin_d001.pdf
About Technosylva
Technosylva is the leading provider of wildfire and extreme weather modeling, risk mitigation, and operational response software. Technosylva’s market-leading solutions, enhanced by AI and machine learning capabilities, provide real-time and predictive insights into developing wildfire and extreme weather risks to support electric utility, insurance, and government agency customers. Founded in 1997, Technosylva has offices in La Jolla, CA, León, Spain, and Calgary, Canada. Learn more at www.Technosylva.com.
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SOURCE Technosylva
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