title: Delineate Anything: Resolution-Agnostic Field Boundary Delineation on
    Satellite Imagery
summary: The accurate delineation of agricultural field boundaries from satellite
  imagery is vital for land management and crop monitoring. However, current
  methods face challenges due to limited dataset sizes, resolution discrepancies,
  and diverse environmental conditions. We address this by reformulating the task
  as instance segmentation and introducing the Field Boundary Instance
  Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset
  comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m
  to 10 m) and 22,926,427 instance masks of individual fields, significantly
  narrowing the gap between agricultural datasets and those in other computer
  vision domains. We further propose Delineate Anything, an instance segmentation
  model trained on our new FBIS-22M dataset. Our proposed model sets a new
  state-of-the-art, achieving a substantial improvement of 88.5% in mAP@0.5 and
  103% in mAP@0.5:0.95 over existing methods, while also demonstrating
  significantly faster inference and strong zero-shot generalization across
  diverse image resolutions and unseen geographic regions. Code, pre-trained
  models, and the FBIS-22M dataset are available at
  https://lavreniuk.github.io/Delineate-Anything.
paper_authors._id: 67f385626d3bac325a2fc231
paper_authors.name: Mykola Lavreniuk
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:52:55.305Z
paper_authors._id: 645229001595002ddc859358
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645229001595002ddc859358/UuewbDDRkruTCvTNQHs2W.jpeg
paper_authors.fullname: Mykola
paper_authors.type: user
paper_authors.user: MykolaL
paper_authors._id: 67f385626d3bac325a2fc232
paper_authors.name: Nataliia Kussul
paper_authors._id: 67f385626d3bac325a2fc233
paper_authors.name: Andrii Shelestov
paper_authors._id: 67f385626d3bac325a2fc234
paper_authors.name: Bohdan Yailymov
paper_authors._id: 67f385626d3bac325a2fc235
paper_authors.name: Yevhenii Salii
paper_authors._id: 67f385626d3bac325a2fc236
paper_authors.name: Volodymyr Kuzin
paper_authors._id: 67f385626d3bac325a2fc237
paper_authors.name: Zoltan Szantoi
paper_title: Delineate Anything: Resolution-Agnostic Field Boundary Delineation on
    Satellite Imagery
paper_summary: The accurate delineation of agricultural field boundaries from satellite
  imagery is vital for land management and crop monitoring. However, current
  methods face challenges due to limited dataset sizes, resolution discrepancies,
  and diverse environmental conditions. We address this by reformulating the task
  as instance segmentation and introducing the Field Boundary Instance
  Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset
  comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m
  to 10 m) and 22,926,427 instance masks of individual fields, significantly
  narrowing the gap between agricultural datasets and those in other computer
  vision domains. We further propose Delineate Anything, an instance segmentation
  model trained on our new FBIS-22M dataset. Our proposed model sets a new
  state-of-the-art, achieving a substantial improvement of 88.5% in mAP@0.5 and
  103% in mAP@0.5:0.95 over existing methods, while also demonstrating
  significantly faster inference and strong zero-shot generalization across
  diverse image resolutions and unseen geographic regions. Code, pre-trained
  models, and the FBIS-22M dataset are available at
  https://lavreniuk.github.io/Delineate-Anything.
paper_ai_summary: A new large-scale dataset and instance segmentation model significantly improve field boundary delineation from satellite imagery in terms of accuracy, inference speed, and zero-shot generalization.
paper_ai_keywords: instance segmentation Field Boundary Instance Segmentation - 22M dataset FBIS-22M Delineate Anything mAP@0.5 mAP@0.5:0.95