FIRETWIN: Digital Twin Advancing Multi-Modal Sensing, Interactive Analytics for Wildfire Response

Current wildfire management systems lack integrated virtual environments that combine historical data with immersive digital representations, hindering deep analysis and effective decision making. This paper introduces FIRETWIN, a cyber-physical Digital Twin (DT) designed to bridge complex ecological data and operationally relevant, high-fidelity visualizations for actionable incident response. FIRETWIN generates a dynamic 3D virtual globe that visualizes evolving fire behavior in real time, driven by output from physics-based fire models. The system supports multimodal perspectives, including satellite and drone viewpoints comparable to NOAA GOES-18 imagery - enabling comprehensive scenario analysis. Users interact with the environment to assess current fire conditions, anticipate progression, and evaluate available resources. Leveraging Google Maps, Unreal Engine, and pre-generated outputs from the CAWFE coupled weather-wildland fire model, we reconstruct the spread of the 2014 King Fire in California Eldorado National Forest. Procedural forest generation and particle-level fire control enable a level of realism and interactivity not possible in field training.