<a href=\"https://cdn-uploads.huggingface.co/production/uploads/645229001595002ddc859358/MojdeKAYQePafTAgGfhBB.jpeg\" rel=\"nofollow\"><img src=\"https://cdn-uploads.huggingface.co/production/uploads/645229001595002ddc859358/MojdeKAYQePafTAgGfhBB.jpeg\" alt=\"label_fixing_pipeline\"></a></p>\n","updatedAt":"2026-07-22T08:48:09.400Z","author":{"_id":"645229001595002ddc859358","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg","fullname":"Mykola Lavreniuk","name":"MykolaL","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":14,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.3577485978603363},"editors":["MykolaL"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.19069","authors":[{"_id":"6a6074377e7f152167e472a3","user":{"_id":"645229001595002ddc859358","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg","isPro":false,"fullname":"Mykola Lavreniuk","user":"MykolaL","type":"user","name":"MykolaL"},"name":"Mykola Lavreniuk","status":"claimed_verified","statusLastChangedAt":"2026-07-22T08:45:04.714Z","hidden":false},{"_id":"6a6074377e7f152167e472a4","name":"Nataliia Kussul","hidden":false},{"_id":"6a6074377e7f152167e472a5","name":"Andrii Shelestov","hidden":false},{"_id":"6a6074377e7f152167e472a6","name":"Yevhenii Salii","hidden":false},{"_id":"6a6074377e7f152167e472a7","name":"Volodymyr Kuzin","hidden":false},{"_id":"6a6074377e7f152167e472a8","name":"Charlotte Julia Li-Xing Wang","hidden":false},{"_id":"6a6074377e7f152167e472a9","name":"Zoltan Szantoi","hidden":false}],"publishedAt":"2026-07-21T00:00:00.000Z","submittedOnDailyAt":"2026-07-22T00:00:00.000Z","title":"Delineate Anything v2: A Global Foundation Model for Field Delineation","submittedOnDailyBy":{"_id":"645229001595002ddc859358","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg","isPro":false,"fullname":"Mykola Lavreniuk","user":"MykolaL","type":"user","name":"MykolaL"},"summary":"Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-area field boundary mapping. We construct FBIS-73M, a 73-million-instance multi-resolution dataset spanning 61 countries. To address the pervasive issue of multi-field administrative parcel merging, we introduce a resolution-specific data curation pipeline that leverages topological image-space adaptation to homogenize merged parcels and strengthen weak physical boundaries. Furthermore, we establish a novel, manually curated evaluation benchmark covering 100 countries to assess independent zero-shot generalization. Our results show that Delineate Anything v2 surpasses the current state-of-the-art, including the Delineate Anything framework, by 0.284
[email protected] (+103.3% relative gain), while maintaining execution speeds suitable for rapid national- and global-scale deployment, as demonstrated by nationwide mapping of Ukraine (603,000 km^2) in 5.4 hours on a consumer-grade workstation. Code, pre-trained weights, the FBIS-73M dataset, and ready-to-use national-scale vector boundary products are publicly available at https://github.com/Lavreniuk/Delineate-Anything.","upvotes":2,"discussionId":"6a6074377e7f152167e472aa"},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"645229001595002ddc859358","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg","isPro":false,"fullname":"Mykola Lavreniuk","user":"MykolaL","type":"user"},{"_id":"6a2da6c8ca070ee12c6e396c","avatarUrl":"/avatars/0355287dcabaa67dbc7f0b10b87451f9.svg","isPro":false,"fullname":"Joe Mama","user":"JoeMama123123123","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.19069.md","query":{}}">
Delineate Anything v2: A Global Foundation Model for Field Delineation
Abstract
Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-area field boundary mapping. We construct FBIS-73M, a 73-million-instance multi-resolution dataset spanning 61 countries. To address the pervasive issue of multi-field administrative parcel merging, we introduce a resolution-specific data curation pipeline that leverages topological image-space adaptation to homogenize merged parcels and strengthen weak physical boundaries. Furthermore, we establish a novel, manually curated evaluation benchmark covering 100 countries to assess independent zero-shot generalization. Our results show that Delineate Anything v2 surpasses the current state-of-the-art, including the Delineate Anything framework, by 0.284 [email protected] (+103.3% relative gain), while maintaining execution speeds suitable for rapid national- and global-scale deployment, as demonstrated by nationwide mapping of Ukraine (603,000 km^2) in 5.4 hours on a consumer-grade workstation. Code, pre-trained weights, the FBIS-73M dataset, and ready-to-use national-scale vector boundary products are publicly available at https://github.com/Lavreniuk/Delineate-Anything.
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Cite arxiv.org/abs/2607.19069 in a model README.md to link it from this page.
Cite arxiv.org/abs/2607.19069 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2607.19069 in a Space README.md to link it from this page.
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