Citation and References¶

Citing Agribound¶

If you use agribound in your research, please cite:

APA:

Majumdar, S., Huntington, J. L., ReVelle, P., Nozari, S., Smith, R. G., Hasan, M. F., Bromley, M., Atkin, J., Jensen, E. R., Ketchum, D., & Roy, S. (2026). Agribound: Unified agricultural field boundary delineation from satellite imagery using geospatial foundation models, pre-trained segmentation, and embeddings [Software]. Zenodo. https://doi.org/10.5281/zenodo.19229665

Majumdar, S., Huntington, J. L., ReVelle, P., Nozari, S., Smith, R. G., Hasan, M. F., Bromley, M., Atkin, J., Jensen, E. R., Ketchum, D., & Roy, S. (2026). Agribound: Unified agricultural field boundary delineation from satellite imagery using geospatial foundation models, pre-trained segmentation, and embeddings. In prep. for Journal of Open Source Software.

References¶

Please also cite the underlying engines and models as appropriate:

Delineate-Anything¶

Lavreniuk, M., Kussul, N., Shelestov, A., Yailymov, B., Salii, Y., Kuzin, V., & Szantoi, Z. (2025). Delineate Anything: Resolution-agnostic field boundary delineation on satellite imagery. arXiv preprint arXiv:2504.02534. https://arxiv.org/abs/2504.02534

Fields of The World (FTW)¶

Kerner, H., Chaudhari, S., Ghosh, A., Robinson, C., Ahmad, A., Choi, E., Jacobs, N., Holmes, C., Mohr, M., Dodhia, R., Lavista Ferres, J. M., & Marcus, J. (2025). Fields of The World: A machine learning benchmark dataset for global agricultural field boundary segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28151--28159. https://doi.org/10.1609/aaai.v39i27.35034

GeoAI¶

Wu, Q. (2026). GeoAI: A Python package for integrating artificial intelligence with geospatial data analysis and visualization. Journal of Open Source Software, 11(118), 9605. https://doi.org/10.21105/joss.09605

DINOv3¶

Siméoni, O., Vo, H. V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D., Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C., Vedaldi, A., ... Bojanowski, P. (2025). DINOv3. arXiv preprint arXiv:2508.10104. https://arxiv.org/abs/2508.10104

Prithvi-EO-2.0¶

Szwarcman, D., Roy, S., Fraccaro, P., et al. (2024). Prithvi-EO-2.0: A versatile multi-temporal foundation model for Earth observation applications. arXiv preprint arXiv:2412.02732. https://arxiv.org/abs/2412.02732

TESSERA¶

Feng, Z., Atzberger, C., Jaffer, S., Knezevic, J., Sormunen, S., Young, R., Lisaius, M. C., Immitzer, M., Jackson, T., Ball, J., Coomes, D. A., Madhavapeddy, A., Blake, A., & Keshav, S. (2025). TESSERA: Temporal embeddings of surface spectra for Earth representation and analysis. arXiv preprint arXiv:2506.20380. https://arxiv.org/abs/2506.20380

geemap¶

Wu, Q. (2020). geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software, 5(51), 2305. https://doi.org/10.21105/joss.02305

SamGeo (Segment Anything for Geospatial Data)¶

Wu, Q., & Osco, L. (2023). samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM). Journal of Open Source Software, 8(89), 5663. https://doi.org/10.21105/joss.05663

Osco, L. P., Wu, Q., de Lemos, E. L., Gonçalves, W. N., Ramos, A. P. M., Li, J., & Junior, J. M. (2023). The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot. International Journal of Applied Earth Observation and Geoinformation, 124, 103540. https://doi.org/10.1016/j.jag.2023.103540

SAM 2¶

Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K. V., Carion, N., Wu, C.-Y., Girshick, R., Dollár, P., & Feichtenhofer, C. (2024). SAM 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. https://arxiv.org/abs/2408.00714

Google Satellite Embeddings (AlphaEarth)¶

Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., Lahera, E., Wiles, O., Ilyushchenko, S., Gorelick, N., Zhang, L. L., Alj, S., Schechter, E., Askay, S., Guinan, O., Moore, R., Boukouvalas, A., & Kohli, P. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv preprint arXiv:2507.22291. https://doi.org/10.48550/arXiv.2507.22291

Google Earth Engine¶

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Awesome GEE Community Catalog¶

Roy, S., Majumdar, S., & Swetnam, T. (2025). samapriya/awesome-gee-community-datasets: Community Catalog (3.9.0). Zenodo. https://doi.org/10.5281/zenodo.17641528

Funding¶

This work was supported by multiple funding sources. The New Mexico Office of the State Engineer (NMOSE) provided reference field boundary data and supported the development of agricultural water use mapping in New Mexico. The Google Satellite Embeddings Dataset Small Grants Program enabled the integration of pre-computed satellite embeddings for unsupervised field boundary delineation. Access to the SPOT 6 and 7 archive on Google Earth Engine was provided through the Google Trusted Tester opportunity. Additional support was provided by the U.S. Army Corps of Engineers and The U.S. Department of Treasury/State of Nevada. This work was also supported by the United States Geological Survey (USGS) and NASA Landsat Science Team, the USGS Water Resources Research Institute, the Desert Research Institute Maki Endowment, and the Windward Fund.

Acknowledgments¶

Agribound builds on the work of many open-source projects and research teams:

The Ultralytics team for YOLOv8/v11 and the broader YOLO ecosystem
Meta AI Research for the Segment Anything Model (SAM)
The Fields of The World consortium and Hannah Kerner's group at Arizona State University
Qiusheng Wu for the GeoAI Python package and field boundary segmentation model
NASA and IBM Research for the Prithvi geospatial foundation model and TerraTorch
Google Research for AlphaEarth satellite embeddings
Feng et al. for the TESSERA foundation model embeddings
The Google Earth Engine team for planetary-scale geospatial computing
The fiboa community for the field boundary schema standard
The TorchGeo team for geospatial deep learning data loaders and utilities
The Desert Research Institute (DRI) for supporting this research

Disclaimer¶

This software is preliminary or provisional and is subject to revision. No warranty, expressed or implied, is made by DRI, USGS, the U.S. Government, or any contributing organization as to the functionality of the software. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. See DISCLAIMER.md for full details.

AI Usage Disclosure¶

Portions of this software were developed with the assistance of AI coding tools, including Anthropic's Claude. AI was used to accelerate code scaffolding, documentation drafting, and test generation. All AI-generated code was reviewed, tested, and validated by the human authors. The scientific methodology, architectural decisions, algorithm selection, and domain-specific implementations reflect the expertise and judgment of the authors.