Primary Convener:
Ziheng Sun, George Mason University Fairfax

Convener:
Yunyao Li, University of Maryland College Park
Daniel Tong, George Mason University
Li Zhang, CU Boulder/CIRES

Early Career Convener:
Yunyao Li, University of Maryland College Park

Chair:
Yunyao Li, University of Maryland College Park
Daniel Tong, George Mason University
Li Zhang, CU Boulder/CIRES
Ziheng Sun, George Mason University

Wildfires are growing in frequency, intensity, and impact—posing an urgent threat to ecosystems, communities, and infrastructure worldwide. This session invites submissions that explore practical, scalable, and actionable approachesto forecasting wildfire spread using advanced geospatial methods, AI/ML models, and integrated sensing technologies.We especially welcome contributions that go beyond theoretical modeling and demonstrate real-world impact through field deployment, decision support, or community engagement. Topics may include, not limited to: Data-driven models for near-term wildfire spread predictionIntegration of remote sensing, meteorological, and topographic dataAI/ML approaches for fire risk forecastingReal-time systems for situational awareness and responseVisualization and user interfaces for decision-makersCommunity-based or low-cost forecasting innovationsEvaluation frameworks and metrics (e.g., accuracy, timeliness)Open-source tools and interoperable solutionsWhether you're building algorithms, deploying tools, or shaping policy, we invite you to share your work in this timely conversation. Let’s build forecasting tools that work not just in the lab—but where and when they’re needed most.

Index Terms
0350 Pressure, density, and temperature
3390 Wildland fire model
4315 Monitoring, forecasting, prediction
4341 Early warning systems

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI

Cross-Listed:
NH - Natural Hazards
IN - Informatics
GH - GeoHealth
GC - Global Environmental Change

Neighborhoods:
3. Earth Covering