用深度学习精准分割全球红树林，提升生态监测效率
A Deep Learning-Based Approach for Mangrove Monitoring
- 构建首个多源红树林数据集MagSet-2，融合全球红树林标注与哨兵2号影像
- Mamba模型在分割任务中全面领先，整体精度达92.3%
- 适合关注生态监测、遥感分析与AI落地的科研与应用人员
红树林是动态的沿海生态系统，对环境健康、经济稳定和气候韧性至关重要。遥感技术在红树林监测与保护中发挥关键作用。本文旨在全面评估最新深度学习模型在红树林分割任务中的表现。我们首次发布一个开源数据集MagSet-2，整合全球红树林观测数据（来自全球红树林观察）与哨兵2号卫星影像，覆盖全球多个红树林分布区。在此基础上，我们对三类架构——卷积神经网络、Transformer与Mamba模型进行了基准测试。实验结果表明，Mamba模型在所有指标上均优于其他架构，验证了其在该任务中的优越性。
原文摘要 · Abstract (English)
Mangroves are dynamic coastal ecosystems that are crucial to environmental health, economic stability, and climate resilience. The monitoring and preservation of mangroves are of global importance, with remote sensing technologies playing a pivotal role in these efforts. The integration of cutting-edge artificial intelligence with satellite data opens new avenues for ecological monitoring, potentially revolutionizing conservation strategies at a time when the protection of natural resources is more crucial than ever. The objective of this work is to provide a comprehensive evaluation of recent deep-learning models on the task of mangrove segmentation. We first introduce and make available a novel open-source dataset, MagSet-2, incorporating mangrove annotations from the Global Mangrove Watch and satellite images from Sentinel-2, from mangrove positions all over the world. We then benchmark three architectural groups, namely convolutional, transformer, and mamba models, using the created dataset. The experimental outcomes further validate the deep learning community's interest in the Mamba model, which surpasses other architectures in all metrics.
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