Pixel-Level Fire Origin Localization via Digital Twin Mapping for Wildfire Surveillance Framework

Wildfire monitoring systems play a critical role in minimizing environmental and societal damage. Recent advances in computer vision, particularly deep learning-based fire detection, have enabled more accurate and scalable solutions. However, conventional fire detection methods often struggle with wildfire scenarios due to wide spatial extent, the demand for precise localization, and the urgency of early response. To overcome these challenges, we propose a wildfire monitoring framework capable of pixel-level fire origin localization mapped onto a GPS-calibrated digital twin of mountainous terrain. Our system integrates visual fire detection with terrain-aware 3D projection, enabling accurate mapping of fire origins to real-world coordinates. Experimental results on wildfire datasets demonstrate that our method achieves high accuracy in both early fire detection and precise localization, offering a practical and scalable solution for real-world wildfire monitoring.