Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914314777001984 |
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| author | Corley, Isaac Kerner, Hannah Robinson, Caleb Marcus, Jennifer |
| author_facet | Corley, Isaac Kerner, Hannah Robinson, Caleb Marcus, Jennifer |
| contents | Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of The World (FTW) ecosystem: a benchmark of 1.6M field polygons across 24 countries, pre-trained segmentation models, and command-line inference tools. We provide two notebooks that cover (1) local-scale field boundary extraction with crop classification and forest loss attribution, and (2) country-scale inference using cloud-optimized data. We use MOSAIKS random convolutional features and FTW derived field boundaries to map crop type at the field level and report macro F1 scores of 0.65--0.75 for crop type classification with limited labels. Finally, we show how to explore pre-computed predictions over five countries (4.76M km\textsuperscript{2}), with median predicted field areas from 0.06 ha (Rwanda) to 0.28 ha (Switzerland). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08131 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries Corley, Isaac Kerner, Hannah Robinson, Caleb Marcus, Jennifer Computer Vision and Pattern Recognition Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of The World (FTW) ecosystem: a benchmark of 1.6M field polygons across 24 countries, pre-trained segmentation models, and command-line inference tools. We provide two notebooks that cover (1) local-scale field boundary extraction with crop classification and forest loss attribution, and (2) country-scale inference using cloud-optimized data. We use MOSAIKS random convolutional features and FTW derived field boundaries to map crop type at the field level and report macro F1 scores of 0.65--0.75 for crop type classification with limited labels. Finally, we show how to explore pre-computed predictions over five countries (4.76M km\textsuperscript{2}), with median predicted field areas from 0.06 ha (Rwanda) to 0.28 ha (Switzerland). |
| title | Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.08131 |