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Congratulations to Yuying Ren and Alek Berg for winning the 2026 Wildland-Urban Interface (WUI) Summit Student Pitch Competition!��

WUI fires present a complex set of challenges that require an interdisciplinary set of solutions. As such, a major goal of the 2026 WUI Fire Engineering Summit is to encourage collaboration between researchers from different engineering subfields and related backgrounds.��

Their proposal "WUI-LLM: A decision Support Dashboard for High-Quality Wildland-Urban Interface Data" won the main prize, courtesy of support provided by the United Engineering Foundation and the SFPE Foundation. The project was supervised by their advisor, Stefan Leyk.

Project Description: Our group has published high-quality and peer-reviewed maps of wildland urban interface (WUI) and fire exposure probability maps through time (1990 to 2020) for WUI properties in the contiguous United States. These maps present a detailed view into how U.S. WUI areas have evolved through time and what present wildland fire exposures they may face. A key gap is the dissemination of our high-fidelity information to decision makers and stakeholders. We propose a unified, decision support dashboard, “WUI-LLM,” that collates these data into a clear presentation of how built environments across the U.S. interact with flammable wildlands. WUI-LLM will map structure level WUI classifications and fire exposure probabilities and allow users to interact with these data using a large-language model enhanced query system to allow for on-the-fly spatial analysis. Our dashboard will remove technical barriers in quantifying community risk to wildland fire and how that risk has changed over time.

WUI White Paper Knowledge Gaps Addressed: Our proposed WUI-LLM decision support dashboard combines peer reviewed high-resolution WUI and wildfire probability layers tied to individual structures across the conterminous U.S. Our proposed work, directly addresses key gaps identified in the white paper, specifically the “lack of standardized methodologies for the calculation of quantitative values of parcel-level fire exposure...”, the need for “categorization of fire exposure scenarios…based on their likelihood of occurring,” and “a need for peer reviewed risk assessment tools at the WUI property level” (Section 3.4). Overarchingly, our temporal WUI (1985-2020) data fill the need for systematic historical datasets to validate wildfire risk and vulnerability (Section 4.4), while our structure-level exposure data respond to the need for accessible parcel-level risk assessments (Risk Assessment at the Parcel Level). WUI-LLM further bridges the gap between complex fire engineering tools and decision-making by enabling interactive, multi-scale analysis through an LLM-driven platform.