用多源卫星数据快速精准绘制野火烧毁区，适合应急响应场景。
Magnifying change: Rapid burn scar mapping with multi-resolution, multi-source satellite imagery
- 融合MODIS与哨兵2号多分辨率影像，实现高时空精度烧毁区检测
- 小范围火灾识别准确率超同类模型，实测在24小时内完成制图
- 开源代码数据，适用于灾害应急监测与长期生态评估
利用卫星影像识别野火影响区域仍面临挑战，因电磁波谱中光谱变化不规则且空间异质性强。尽管近期深度学习方法在高分辨率多光谱数据下表现优异，但在实际操作中，受限于当前卫星系统在空间分辨率与重访频率间的权衡，难以实现灾后快速制图。为此，本文提出新型深度学习模型BAM-MRCD，融合多分辨率、多源卫星影像（MODIS与哨兵2号），实现高时空分辨率的详细烧毁区地图生成。该模型能以高精度检测微小规模野火，性能超越现有变化检测模型及基线方法。所有数据与代码已公开于GitHub：https://github.com/Orion-AI-Lab/BAM-MRCD。
原文摘要 · Abstract (English)
Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. While recent deep learning approaches achieve high accuracy when high-resolution multispectral data are available, their applicability in operational settings, where a quick delineation of the burn scar shortly after a wildfire incident is required, is limited by the trade-off between spatial resolution and temporal revisit frequency of current satellite systems. To address this limitation, we propose a novel deep learning model, namely BAM-MRCD, which employs multi-resolution, multi-source satellite imagery (MODIS and Sentinel-2) for the timely production of detailed burnt area maps with high spatial and temporal resolution. Our model manages to detect even small scale wildfires with high accuracy, surpassing similar change detection models as well as solid baselines. All data and code are available in the GitHub repository: https://github.com/Orion-AI-Lab/BAM-MRCD.
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