用卫星影像和深度学习识别放牧区域，提升监管效率。
Grazing Detection using Deep Learning and Sentinel-2 Time Series Data
- 结合卷积与循环神经网络分析多时相遥感数据
- 平均F1达77%，未放牧牧场召回率90%
- 可使检查效率提升17.2倍，适合环保监管者使用
放牧影响农业生产与生物多样性，但大范围监测仍受限。本文利用哨兵-2 L2A时序数据，对每个地块边界在4月至10月的影像进行二分类（放牧/未放牧）预测。通过训练集成的CNN-LSTM模型，在多时相反射率特征上取得平均F1分数77%，对放牧牧场的召回率达90%。实际应用中，若巡查员每年仅能访问4%的地点，优先检查模型预测为未放牧的地块，可获得随机抽查17.2倍的确认未放牧站点数。结果表明，高分辨率、免费获取的卫星数据可有效引导监管资源，支持生态保护导向的土地合规检查。代码与模型已公开。
原文摘要 · Abstract (English)
Grazing shapes both agricultural production and biodiversity, yet scalable monitoring of where grazing occurs remains limited. We study seasonal grazing detection from Sentinel-2 L2A time series: for each polygon-defined field boundary, April-October imagery is used for binary prediction (grazed / not grazed). We train an ensemble of CNN-LSTM models on multi-temporal reflectance features, and achieve an average F1 score of 77 percent across five validation splits, with 90 percent recall on grazed pastures. Operationally, if inspectors can visit at most 4 percent of sites annually, prioritising fields predicted by our model as non-grazed yields 17.2 times more confirmed non-grazing sites than random inspection. These results indicate that coarse-resolution, freely available satellite data can reliably steer inspection resources for conservation-aligned land-use compliance. Code and models have been made publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。