The first global agricultural field boundary map at 10m resolution

• URL: http://arxiv.org/abs/2605.11055v1
• Date: Mon, 11 May 2026 15:54:42 GMT
• Title: The first global agricultural field boundary map at 10m resolution
• Abstract summary: Most global remote-sensing products for agriculture are only available at the pixel level.<n>No openly available, globally consistent map of agricultural field boundaries exists to date.<n>We present the first global field boundary dataset at 10,m resolution for the years 2024 and 2025.
• Score: 23.150413419951388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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