用机器学习自动识别大型哺乳动物啃食路径，助力生态监测
Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals
- 用5种分割模型+14种编码器测试，选最优组合
- UNet搭配MambaOut编码器效果最佳，可精准识别路径
- 适合生态学家和遥感研究人员做长期追踪
识别生物多样性受威胁的区域对有效生态系统保护与监测至关重要。本研究评估了多种机器学习方法以实现对放牧路径的自动检测。我们测试了五种语义分割模型与十四种不同的编码器网络。最佳组合为UNet搭配MambaOut编码器。所提出的方案可作为工具基础，用于在连续时间基础上绘制和追踪放牧路径的变化。
原文摘要 · Abstract (English)
Identifying spatial regions where biodiversity is threatened is crucial for effective ecosystem conservation and monitoring. In this stydy, we assessed varios machine learning methods to detect grazing trails automatically. We tested five semantic segmentation models combined with 14 different encoder networks. The best combination was UNet with MambaOut encoder. The solution proposed could be used as the basis for tools aiming at mapping and tracking changes in grazing trails on a continuous temporal basis.
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