Wildfires have wreaked havoc on many communities across the globe in recent years. Despite a decline in global burned area over the past two decades, the number of people directly exposed to fires increased by 40%. Disastrous wildfires have also escalated in the past decade, with clear links to climatic extremes. Remote sensing observations across diverse platforms–delivering consistent, scalable, and near-real-time monitoring capabilities–combined with new global data sets, advances in AI, advanced geospatial techniques, and the availability of computational resources, have opened new, exciting pathways for wildfire detection and impact assessment. This session seeks presentations that explore: (1) Fire-related Multi-modal Data Fusion Across Scales, involving optical, thermal, SAR, LiDAR, and hyperspectral sensors from polar orbiting and geostationary satellites, CubeSats, and drones, (2) Foundation Models for Fire, Digital Twins, GenAI, and Other Predictive Models, (3) Human-Fire-Infrastructure Coupled Systems, (4) Causal AI for Fire, and (5) Coupled Fire-Atmosphere-Health Modeling, among others.

• B - Biogeosciences
• GC - Global Environmental Change
• IN - Informatics
• SY - Science and Society