全球首个10米分辨率的农业田界地图
The first global agricultural field boundary map at 10m resolution
AI总结：
本文提出首个全球10米分辨率农业田界地图，基于Sentinel-2影像和U-Net模型生成31.7亿个田块，通过验证达到85%召回率，提供全球一致的农业监测数据。
摘要：
农业田块是种植、管理、监管和报告的自然单位，但大多数全球遥感农业产品仅在像素级别可用。虽然存在高质量田级数据产品，但它们仅来自欧洲部分地区或个别国家的ML衍生产品。目前尚无公开可用的全球一致农业田界地图。本文提出了首个全球田界数据集，分辨率10米，涵盖2024和2025年241个国家和领土，包含31.7亿个遥感田块，由U-Net分割模型处理无云Sentinel-2镶嵌影像生成。在24个国家的实地验证中，地图在像素级召回率平均达0.85，14个国家超过0.90。在奥地利、拉脱维亚和芬兰的全国家庭验证中，F1分数分别为0.89、0.88和0.74。由于全球验证参考数据不完整，我们随地图附带500米信心层，识别预测可靠的区域。数据集以三种全球地图形式发布：默认字段边界数据集（经过信心阈值处理）、完整未过滤数据集和连续值信心栅格。这些地图为作物监测、粮食安全和下游农业科学提供了首个全球一致的田级分析单位。
英文摘要：
The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available at the pixel level. While some high-quality field-level data products exist, they come from parcel registries covering only parts of Europe or from ML-derived products for individual countries. No openly available, globally consistent map of agricultural field boundaries exists to date. Here we present the first global field boundary dataset at 10\,m resolution for the years 2024 and 2025, comprising 3.17 billion remote-sensing field polygons (1.62 B in 2024 and 1.55 B in 2025) across 241 countries and territories, produced by applying a U-Net segmentation model trained on the Fields of The World dataset to cloud-free Sentinel-2 mosaics. Validated against ground-truth field boundaries in 24 countries, the map achieved a mean pixel-level recall of 0.85 with 14 countries exceeding 0.90. Evaluation against full-country ground-truth datasets in Austria, Latvia, and Finland yielded F1 scores of 0.89, 0.88, and 0.74, respectively. Because reference data for global validation is inherently incomplete, we accompanied the map with a 500 m confidence layer that identifies regions where predictions are reliable. We release the dataset openly as three global maps: the confidence-thresholded default field boundary dataset, the full unfiltered dataset, and the continuous-valued confidence raster. These maps provide the first globally consistent field-level unit of analysis for crop monitoring, food security, and downstream agricultural science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
