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

Mayamin Hamid Raha, Ali Reza Tavakkoli, Chris Webb, Mobin Habibpour, Janice Coen, Eric Rowell, Fatemeh Afghah

TL;DR

FIRETWIN addresses the lack of integrated, immersive wildfire analysis tools by presenting a cyber-physical Digital Twin that fuses physics-based CAWFE fire modeling with a high-fidelity, geo-synchronized visualization platform. Leveraging Unreal Engine 5, procedural forest generation, and UAV-simulated sensing, the approach enables real-time, particle-level visualization of fire spread across large geographies and multiple perspectives, including satellite and VR interfaces. The key contributions include a real-time CAWFE-to-particle mapping using $F_{norm}$ and $F_{curved}$, fuel-density driven PCG for forests, and adaptive LOD for scalable presentation, validated through reconstruction of the 2014 King Fire. The resulting system enhances decision support, training, and post-incident analysis by providing an immersive, data-driven environment for tactical wildfire management.

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.

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

TL;DR

FIRETWIN addresses the lack of integrated, immersive wildfire analysis tools by presenting a cyber-physical Digital Twin that fuses physics-based CAWFE fire modeling with a high-fidelity, geo-synchronized visualization platform. Leveraging Unreal Engine 5, procedural forest generation, and UAV-simulated sensing, the approach enables real-time, particle-level visualization of fire spread across large geographies and multiple perspectives, including satellite and VR interfaces. The key contributions include a real-time CAWFE-to-particle mapping using and , fuel-density driven PCG for forests, and adaptive LOD for scalable presentation, validated through reconstruction of the 2014 King Fire. The resulting system enhances decision support, training, and post-incident analysis by providing an immersive, data-driven environment for tactical wildfire management.

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.
Paper Structure (18 sections, 5 equations, 7 figures, 1 table, 2 algorithms)

This paper contains 18 sections, 5 equations, 7 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. FIRETWIN Methodology
  4. System Design and Implementation
  5. Fire Simulation
  6. Mathematical Framework and Calibration
  7. Procedural Generation of Forest
  8. Multi-modal Data Integration
  9. User Interface Design Methodology:
  10. Virtual Reality
  11. Validation Approach
  12. Results
  13. System Performance and Technical Capabilities
  14. Procedural Forest Reconstruction
  15. Historical Fire Reconstruction
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Procedural Forest with High Level of Details (top left), different types of fire spread scenarios (top right, bottom left), and Perimeter of Historic King Fire Reconstruction (bottom right) in FIRETWIN.
  • Figure 2: High-level data flow within the proposed FIRETWIN environment for wildfire analysis and tactical decision-making.
  • Figure 3: King Fire reconstruction in procedurally generated Eldorado National Forest.
  • Figure 4: Multi-modal RGB, Satellite and Thermal view Integration in FIRETWIN
  • Figure 5: UI featuring point of interest boxes attached to geo-synchronized 3D Firestation models.
  • ...and 2 more figures