用双时相网络和大模型数据集，实现高精度自动烧毁区域识别。
Deep Learning-Based Burned Area Mapping Using Bi-Temporal Siamese Networks and AlphaEarth Foundation Datasets
- 采用双时相孪生U-Net结构，对比前后影像差异
- 在欧美17个区域测试，准确率达95%，交并比0.6
- 适合需要全球火情监测的环境与应急部门
精准及时地绘制烧毁区域对环境监测、灾害管理和气候变化评估至关重要。本研究提出一种基于AlphaEarth数据集与孪生U-Net深度学习架构的自动化烧毁区域映射新方法。AlphaEarth数据集包含高分辨率光学与热红外影像及全面的地面真值标注，为训练鲁棒的烧毁区域检测模型提供了前所未有的资源。模型在美国内陆地区使用MTBS数据集训练，并在欧洲17个区域进行跨区域验证。实验结果表明，所提集成方法在测试数据集上整体准确率达95%，交并比（IoU）为0.6，F1分数达74%。模型能有效识别多样生态系统中复杂的背景下的烧毁区域，尤其擅长检测部分燃烧植被与火线边界，展现出良好的迁移能力与泛化性能。该研究推动了自动化火灾损毁评估的发展，为利用AlphaEarth数据集实现全球烧毁区域监测提供了可扩展解决方案。
原文摘要 · Abstract (English)
Accurate and timely mapping of burned areas is crucial for environmental monitoring, disaster management, and assessment of climate change. This study presents a novel approach to automated burned area mapping using the AlphaEArth dataset combined with the Siamese U-Net deep learning architecture. The AlphaEArth Dataset, comprising high-resolution optical and thermal infrared imagery with comprehensive ground-truth annotations, provides an unprecedented resource for training robust burned area detection models. We trained our model with the Monitoring Trends in Burn Severity (MTBS) dataset in the contiguous US and evaluated it with 17 regions cross in Europe. Our experimental results demonstrate that the proposed ensemble approach achieves superior performance with an overall accuracy of 95%, IoU of 0.6, and F1-score of 74% on the test dataset. The model successfully identifies burned areas across diverse ecosystems with complex background, showing particular strength in detecting partially burned vegetation and fire boundaries and its transferability and high generalization in burned area mapping. This research contributes to the advancement of automated fire damage assessment and provides a scalable solution for global burn area monitoring using the AlphaEarth dataset.
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