GPU-accelerated visualization of historical wildfire events with temporal geospatial data integration

Area of Science:

• Computer Science
• Geospatial Analysis
• Scientific Visualization

Background:

• Wildfires pose significant threats to ecosystems and human populations.
• Existing tools for wildfire reconstruction often lack integration and real-time performance.
• Effective visualization aids emergency services in hazard assessment and responder training.

Purpose of the Study:

• To present a GPU-accelerated Unity system for precise temporal replay and rendering of documented wildfire events.
• To develop a visualization pipeline for historical fire records, focusing on recorded event data.
• To enhance wildfire analysis and training through realistic simulations.

Main Methods:

• Developed a GPU-accelerated visualization pipeline within the Unity engine.
• Integrated geospatial processing, particle rendering, and temporal event modeling.
• Implemented a geodetic transformation pipeline for coordinate consistency and a deterministic temporal scheduler for timestamp-faithful replay.
• Incorporated wind data and performance optimizations like object pooling and GPU-accelerated particle rendering.

Main Results:

• Successfully reconstructed documented wildfire patterns with temporal precision.
• Achieved stable, real-time frame rates even in high-density fire scenarios (up to 407 simultaneous fires).
• Demonstrated the system's capability on both dedicated and integrated GPUs.

Conclusions:

• The developed system provides a powerful tool for scientific analysis of historical wildfire behavior.
• The real-time performance and accuracy make it highly applicable for emergency responder training.
• This visualization pipeline offers a significant advancement over existing wildfire analysis tools.