用深度学习生成英格兰25厘米高分辨率农田景观图，助力生态保护
Mapping Farmed Landscapes from Remote Sensing
- 基于942张航拍图人工标注数据，训练深度分割模型
- 林地和农田识别F1达96%和95%，篱笆分割准确率72%
- 地图开源可查，适合生态规划与政策制定者使用
有效管理农业景观对实现全球生物多样性目标至关重要，但缺乏详细的大规模生态地图制约了进展。为此，我们提出Farmscapes——首个覆盖英格兰大部分地区、分辨率高达25cm的农村景观特征地图，包含对生态至关重要的篱笆、林地和石墙等要素。该地图基于一个深度学习分割模型生成，该模型在由942个手工标注的航拍图像块组成的新型数据集上训练。模型在关键生境识别上表现优异，林地和农用地的F1分数分别达到96%和95%，对线性特征（如篱笆）的分割也表现出色，F1得分为72%。通过在Google Earth Engine上发布英格兰全域地图，我们提供了一个强大且开放获取的工具，支持生态学家和政策制定者进行数据驱动的栖息地恢复规划，助力欧盟生物多样性战略等行动，并为景观连通性分析奠定基础。
原文摘要 · Abstract (English)
Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological maps. To address this, we introduce Farmscapes, the first large-scale (covering most of England), high-resolution (25cm) map of rural landscape features, including ecologically vital elements like hedgerows, woodlands, and stone walls. This map was generated using a deep learning segmentation model trained on a novel, dataset of 942 manually annotated tiles derived from aerial imagery. Our model accurately identifies key habitats, achieving high f1-scores for woodland (96\%) and farmed land (95\%), and demonstrates strong capability in segmenting linear features, with an F1-score of 72\% for hedgerows. By releasing the England-wide map on Google Earth Engine, we provide a powerful, open-access tool for ecologists and policymakers. This work enables data-driven planning for habitat restoration, supports the monitoring of initiatives like the EU Biodiversity Strategy, and lays the foundation for advanced analysis of landscape connectivity.
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