Delineate Anything: Resolution-Agnostic Field Boundary Delineation on
  Satellite Imagery
Authors: name: Mykola Lavreniuk, name: Nataliia Kussul, name: Andrii Shelestov, name: Bohdan Yailymov, name: Yevhenii Salii, name: Volodymyr Kuzin, name: Zoltan Szantoi

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.