Semantic Segmentation of Fire Fronts in Helicopter Imagery Using Vision Transformer Models

Abstract

Rapid and accurate identification of fire fronts is essential for determining the extent and direction of wildfire spread, which is critical for developing effective suppression strategies.We created a comprehensive fire front dataset, containing 7,869 mask-labeled images extracted from helicopter footage captured during actual wildfire suppression operations.This study employed segmentation models based on transformer architectures to analyze helicopter imagery, specifically targeting regions of active fire and ground-level smoke.The Swin Transformer and Mask2Former models were trained, and their performances were compared.We evaluated model performance using both quantitative metrics and qualitative visual assessments.While the mean intersection over union scores achieved by the models were moderate, at 0.621 and 0.641, respectively, subsequent post-processing allowed for the extraction of fire front patterns closely aligned with real-world labeled data.Our future research aims to enhance dataset quality further and improve model accuracy, ultimately contributing to a robust and practical automated system for fire front detection.