FBIS-73M

Field Boundary Instance Segmentation - 73M dataset (FBIS-73M) - large-scale, multi-resolution dataset comprising 1,478,096 high-resolution satellite image patches (0.25 m – 10 m) and 74,015,889 instance masks of individual fields.

Dataset Structure

The dataset has three splits: train, test, and a 100-country zero-shot test set. Each split is described by a list file, and the actual image/label data is distributed as independent zip archives.

File	Description
train.txt	Training image filenames — 1,432,187 images
test.txt	Test image filenames — 45,509 images
test_zeroshot_100.txt	Zero-shot test image filenames — 400 images (4 per country, 100 countries)

Archive	Contents	Size
FBIS-73M_train_part-01-of-20.zip … FBIS-73M_train_part-20-of-20.zip	Train images + labels, split into 20 parts	~24 GB each (part 20 ~16.9 GB)
FBIS-73M_test.zip	Test images + labels	~18 GB
FBIS-73M_test_zeroshot_100.zip	Zero-shot test images + labels	~90 MB

Each archive is self-contained — it holds its own images/ and labels/ folders with matching pairs, so no archive depends on another. You only need to download the parts you actually want to use.

Labels are YOLO-format instance segmentation polygons (one .txt per image, same filename, one row per field instance). Images with no fields present have an empty .txt label.

How to Use

Download any archive(s) you need and extract them:

unzip FBIS-73M_train_part-01-of-20.zip

Extracting multiple train parts into the same directory merges their images//labels/ folders together.

Citation

If you find our dataset useful in your research, please consider citing:

@inproceedings{lavreniuk2026delanyv2,
      title={Delineate Anything v2: A Global Foundation Model for Field Delineation}, 
      author={Mykola Lavreniuk and Nataliia Kussul and Andrii Shelestov and Yevhenii Salii and Volodymyr Kuzin and Charlotte Julia Li-Xing Wang and Zoltan Szantoi},
      year={2026},
      booktitle={European Conference on Computer Vision Workshops (ECCVW)},
      note={arXiv:2607.19069},
}

@inproceedings{lavreniuk2025delineateanything,
      title={Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery}, 
      author={Mykola Lavreniuk and Nataliia Kussul and Andrii Shelestov and Bohdan Yailymov and Yevhenii Salii and Volodymyr Kuzin and Zoltan Szantoi},
      year={2025},
      booktitle={European Conference on Artificial Intelligence},
      note={arXiv:2504.02534},
}

@article{lavreniuk2025delineateanythingflow,
      title={Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source}, 
      author={Mykola Lavreniuk and Nataliia Kussul and Andrii Shelestov and Yevhenii Salii and Volodymyr Kuzin and Sergii Skakun and Zoltan Szantoi},
      year={2025},
      journal={https://arxiv.org/abs/2511.13417},
}