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Citation and References

Citing agribound

If you use agribound in your research, please cite the software (the DOI below is the Zenodo concept DOI, which resolves to the latest version):

Majumdar, S., Rapp, J., Huntington, J. L., ReVelle, P., Nozari, S., Smith, R. G., Hasan, M. F., Bromley, M., Atkin, J., Jensen, E. R., Ketchum, D., Abramowitz, J. C., & Roy, S. (2026). Agribound: Unified agricultural field boundary delineation from satellite imagery using geospatial foundation models, pre-trained segmentation, and embeddings (Version 1.0.1) [Software]. Zenodo. https://doi.org/10.5281/zenodo.19229665

The accompanying manuscript is in preparation:

Majumdar, S., Rapp, J., 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). Measuring what geospatial AI delivers for policy-grade agricultural field boundaries. In prep. for Remote Sensing of Environment.

Machine-readable metadata: CITATION.cff.

References

Please also cite the models, datasets and tools you use. The entries below were checked against Crossref, DataCite, the arXiv API or the publishers' pages in September 2026; preprints without a peer-reviewed version are cited as preprints.

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. European Conference on Artificial Intelligence (ECAI 2025). arXiv:2504.02534. https://doi.org/10.48550/arXiv.2504.02534 (models large, small, dataset FBIS-22M)

Lavreniuk, M., Kussul, N., Shelestov, A., Salii, Y., Kuzin, V., Wang, C. J. L.-X., & Szantoi, Z. (2026). Delineate Anything v2: A global foundation model for field delineation. European Conference on Computer Vision Workshops (ECCVW 2026, GAIA workshop). arXiv:2607.19069. https://doi.org/10.48550/arXiv.2607.19069 (model large_v2, dataset FBIS-73M)

Lavreniuk, M., Kussul, N., Shelestov, A., Salii, Y., Kuzin, V., Skakun, S., & Szantoi, Z. (2025). Delineate Anything Flow: Fast, country-level field boundary detection from any source. arXiv:2511.13417. https://doi.org/10.48550/arXiv.2511.13417 (the reference pipeline behind backend="reference")

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

Muhawenayo, G., Robinson, C., Khanal, S., Fang, Z., Corley, I., Wollam, A., Gao, T., Strnad, L., Avery, R., Estes, L., Tárano, A. M., Jacobs, N., & Kerner, H. (2026). PRUE: A practical recipe for field boundary segmentation at scale. arXiv:2603.27101. https://doi.org/10.48550/arXiv.2603.27101 (the FTW_PRUE_* models)

Robinson, C., Muhawenayo, G., Khanal, S., Fang, Z., Corley, I., Tárano, A. M., Estes, L., Marcus, J., Jacobs, N., Kerner, H., Becker-Reshef, I., & Lavista Ferres, J. M. (2026). The first global agricultural field boundary map at 10m resolution. arXiv:2605.11055 (preprint). https://doi.org/10.48550/arXiv.2605.11055 (the published FTW polygons read by query_ftw; dataset CC-BY-4.0)

GeoAI and Mask R-CNN

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

He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2980-2988. https://doi.org/10.1109/ICCV.2017.322

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., Tolan, J., Brandt, J., Couprie, C., Mairal, J., Jégou, H., Labatut, P., & Bojanowski, P. (2025). DINOv3. arXiv:2508.10104. https://doi.org/10.48550/arXiv.2508.10104 (The DINOv3 weights are under the DINOv3 License, whose clause 1.b.ii requires publications to acknowledge the use of DINO Materials.)

Prithvi-EO-2.0 and TerraTorch

Szwarcman, D., Roy, S., Fraccaro, P., Gíslason, Þ. E., Blumenstiel, B., Ghosal, R., de Oliveira, P. H., de Sousa Almeida, J. L., Sedona, R., Kang, Y., Chakraborty, S., Wang, S., Gomes, C., Kumar, A., Gaur, V., Truong, M., Godwin, D., Khallaghi, S., Lee, H., Hsu, C.-Y., Akbari Asanjan, A., Mujeci, B., Shidham, D., Balogun, R. O., Kolluru, V., Keenan, T., Arevalo, P., Li, W., Alemohammad, H., Olofsson, P., Mayer, T., Hain, C., Kennedy, R., Zadrozny, B., Bell, D., Cavallaro, G., Watson, C., Maskey, M., Ramachandran, R., & Bernabe Moreno, J. (2026). Prithvi-EO-2.0: A versatile multitemporal foundation model for Earth observation applications. IEEE Transactions on Geoscience and Remote Sensing, 64, 1-20. https://doi.org/10.1109/TGRS.2025.3642610

Gomes, C., Blumenstiel, B., de Sousa Almeida, J. L., de Oliveira, P. H., Fraccaro, P., Marti Escofet, F., Szwarcman, D., Simumba, N., Kienzler, R., & Zadrozny, B. (2025). TerraTorch: The geospatial foundation models toolkit. IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, 6364-6368. https://doi.org/10.1109/IGARSS55030.2025.11243570

Embeddings

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. (2026). TESSERA: Temporal embeddings of surface spectra for Earth representation and analysis. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 34818-34831. arXiv:2506.20380.

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:2507.22291. https://doi.org/10.48550/arXiv.2507.22291. The AlphaEarth Foundations Satellite Embedding dataset is produced by Google and Google DeepMind (CC-BY 4.0).

Segment Anything and samgeo

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. (2025). SAM 2: Segment anything in images and videos. International Conference on Learning Representations (ICLR 2025). arXiv:2408.00714.

Carion, N., Gustafson, L., Hu, Y.-T., Debnath, S., Hu, R., Suris, D., Ryali, C., Alwala, K. V., Khedr, H., Huang, A., Lei, J., Ma, T., Guo, B., Kalla, A., Marks, M., Greer, J., Wang, M., Sun, P., Rädle, R., Afouras, T., Mavroudi, E., Xu, K., Wu, T.-H., Zhou, Y., Momeni, L., Hazra, R., Ding, S., Vaze, S., Porcher, F., Li, F., Li, S., Kamath, A., Cheng, H. K., Dollár, P., Ravi, N., Saenko, K., Zhang, P., & Feichtenhofer, C. (2026). SAM 3: Segment anything with concepts. International Conference on Learning Representations (ICLR 2026). arXiv:2511.16719. (The SAM 3 licence asks publications to acknowledge the use of SAM materials.)

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment anything. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 3992-4003. https://doi.org/10.1109/ICCV51070.2023.00371

Wu, Q., & Osco, L. P. (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., & Marcato Junior, J. (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

Data and platforms

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

Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. https://doi.org/10.1016/j.rse.2018.09.002

Brown, C. F., Brumby, S. P., Guzder-Williams, B., et al. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. Scientific Data, 9, 251. https://doi.org/10.1038/s41597-022-01307-4

Pasquarella, V. J., Brown, C. F., Czerwinski, W., & Rucklidge, W. J. (2023). Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning. Proceedings of the IEEE/CVF CVPR Workshops, 2125-2135. https://doi.org/10.1109/CVPRW59228.2023.00206 (Cloud Score+)

U.S. Geological Survey (2024). Annual NLCD Collection 1 Science Products (ver. 1.2, June 2026). U.S. Geological Survey data release. https://doi.org/10.5066/P94UXNTS

Copernicus Climate Change Service (2019). Land cover classification gridded maps from 1992 to present derived from satellite observations. ECMWF Climate Data Store. https://doi.org/10.24381/cds.006f2c9a

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 (Annual NLCD and C3S assets used by the LULC filter)

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

Evaluation

Clinton, N., Holt, A., Scarborough, J., Yan, L., & Gong, P. (2010). Accuracy assessment measures for object-based image segmentation goodness. Photogrammetric Engineering & Remote Sensing, 76(3), 289-299. https://doi.org/10.14358/PERS.76.3.289

Persello, C., & Bruzzone, L. (2010). A novel protocol for accuracy assessment in classification of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1232-1244. https://doi.org/10.1109/TGRS.2009.2029570

Stehman, S. V., & Foody, G. M. (2019). Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199. https://doi.org/10.1016/j.rse.2019.05.018

HPC

Boerner, T. J., Deems, S., Furlani, T. R., Knuth, S. L., & Towns, J. (2023). ACCESS: Advancing innovation: NSF's Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support. Practice and Experience in Advanced Research Computing (PEARC '23), 173-176. https://doi.org/10.1145/3569951.3597559

Work that uses NSF ACCESS resources must include the acknowledgement at https://access-ci.org/about/acknowledging-access/; system papers are listed in examples/hpc/README.md.

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 NASA Water Resources Applications Program, 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 the YOLO ecosystem
  • Meta AI Research for the Segment Anything models (SAM, SAM 2, SAM 3) and DINOv3
  • The Fields of The World consortium and Hannah Kerner's group at Arizona State University
  • Mykola Lavreniuk and co-authors for Delineate-Anything
  • Qiusheng Wu for the GeoAI and samgeo Python packages
  • NASA and IBM Research for the Prithvi geospatial foundation model and TerraTorch
  • Google DeepMind 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.