Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery

• URL: http://arxiv.org/abs/2609.01392v1
• Date: Tue, 01 Sep 2026 15:20:19 GMT
• Title: Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
• Abstract summary: Active wildfire mapping from satellite imagery is challenging due to the sparse and highly imbalanced nature of fire pixels.<n>This work investigates the use of multispectral Landsat-8 imagery for active-fire segmentation under multi-scale wildfire size conditions.
• Score: 3.3831247297841798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active wildfire mapping from satellite imagery is challenging due to the sparse and highly imbalanced nature of fire pixels, especially in early-stage or low-density fire observations. This work investigates the use of multispectral Landsat-8 imagery for active-fire segmentation under multi-scale wildfire size conditions. We propose a data-driven protocol to characterize fire-region size distributions through connected-component analysis and an interquartile range criterion, enabling the evaluation of model robustness across different local fire-region densities. Three segmentation architectures, U-Net, DeepLabV3+, and SegFormer, are evaluated under different SWIR-based spectral configurations. Results show that U-Net achieves the strongest robustness across the evaluated conditions, SegFormer provides competitive performance, and DeepLabV3+ tends to produce conservative predictions with reduced recall. Across architectures, SWIR2 consistently achieves the strongest or near-best results, highlighting its importance for active-fire segmentation in Landsat-8 imagery. These findings suggest that both spectral band selection and architectural design are critical for robust satellite-based active wildfire mapping trained on low active fire-pixel density images.

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