Problem
Research questions and friction points this paper is trying to address.
wildfire segmentation
satellite imagery
sparse fire pixels
imbalanced data
early-stage fires
Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
🤖 AI Summary
研究使用多尺度Landsat-8影像和三种分割架构（U-Net、DeepLabV3+、SegFormer）解决稀疏不平衡的活跃野火像素分割问题，发现U-Net表现最佳。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Scale-based Approach
Multispectral Landsat-8 Imagery
Connected-component Analysis
Interquartile Range Criterion
Spectral Band Selection
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