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

TL;DR

FIRETWIN is a digital twin system that integrates ecological data and high-fidelity visualizations to improve wildfire response through real-time 3D visualization and interactive analysis.

Contribution

The paper presents FIRETWIN, a novel cyber-physical digital twin that combines physics-based fire modeling with immersive visualizations for wildfire management.

Findings

Real-time 3D visualization of fire behavior

Multimodal perspectives including satellite and drone views

Enhanced interactivity for fire assessment and resource planning

Abstract

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.