FIRE-WUI: A Framework for Community Wildfire Resilience, Readiness, and Recovery

Wildfires in the western United States are causing increasing economic and environmental harm, especially in communities where wildlands and urban areas meet. This project addresses a national priority by developing an innovative framework to help communities reduce wildfire risk, plan for resilience, and recover after fire events. The framework empowers local governments, planners, and residents with tools to help make informed decisions that protect lives, property, and critical infrastructure. By integrating real-time data, advanced modeling, and community engagement, the framework supports national efforts to strengthen disaster resilience and reduce the impacts of catastrophic wildfires. The project brings together experts in fire science, urban planning, computer science, engineering, and social science to create a dynamic, context-aware model of the wildland-urban interface. Unlike traditional static maps, this model uses Earth observation data, simulation, and machine learning to identify at-risk zones and forecast wildfire impacts. The framework includes a risk assessment tool, a multi-scale mitigation strategy evaluator, and a readiness and recovery planning guide. These tools allow communities to compare the effectiveness of different interventions, such as home hardening and vegetation management, and to plan for both immediate response and long-term recovery. The project also incorporates lessons learned from recent wildfires, ensuring that real-world experiences shape future planning. Through workshops and focus groups, the project will engage stakeholders and build local capacity, translating technical advances in artificial intelligence and wildfire modeling into practical, actionable strategies for resilience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.