Geospatial Innovation for Food Security
Fields of The World
Features of The World
Benchmarks of The World
Fields of The World is a groundbreaking program designed as an “innovation bridge” between academic research and industry. In its first phase, FTW released the largest benchmark dataset for training models to infer field boundaries from satellite imagery.
In its second phase, FTW released an assessment of over 70 geospatial AI models and their comparative performance, in addition to releasing a high performing model architecture for inferring global field boundaries. By making this ecosystem openly available, we’re empowering researchers, NGOs, and governments to better understand and manage agricultural systems worldwide.
Related Papers
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. AAAI Conference on Artificial Intelligence.
Corley, I., Kerner, H., Robinson, C., & Marcus, J. (2026). Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries. ICLR Workshop on Machine Learning for Remote Sensing.
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. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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 preprint arXiv:2605.11055.
User Advisory Group
The Fields of The World User Advisory Group provides feedback on the FTW model, data products, workflows, documentation, and research priorities while serving as pilot partners for new datasets and tools. These experts represent diverse sectors using geospatial data to address critical issues like sustainable agriculture, deforestation, natural resource management, and food security.
- Ariel Zajdband, Planet
- Austin Arrington, MillPont
- Brett Lord-Castillo, Bayer
- Dave McCaffrey, Miraterra Soil
- Elinor Benami, Virginia Tech and NASA Harvest
- Giancarlo Pini, World Food Programme
- Gilberto Câmara, INPE (National Institute for Space Research – Brazil) and UN FAO
- Ignacio Ciampitti, Purdue University and NASA Acres
- José Volante, INTA (National Agricultural Technology Institute – Argentina)
- Kai Sonder, CIMMYT (International Maize and Wheat Improvement Center – Mexico)
- Kenneth Mwangi, World Resources Institute
- Rhiannon Rognstad, World Resources Institute
- Robin Cole, EarthDaily
Technical Fellows
Taylor Geospatial uses technical fellows to bridge the gap between academic research and impactful use. Fellows are elite geospatial experts who work closely with research teams.
Their role ensures that complex science translates into products and tools the industry can actually use. For example, on large projects like global field boundaries mapping, fellows work shoulder-to-shoulder with academic modelers to scale the data for planetary use.
Get Involved
Fields of The World is built on open collaboration. Connect with the team and join us in our mission to use AI and ML to identify important information in satellite imagery at global scale.
Community Experts Help Fields of The World Flourish
Other Inititatives
Features of The World
Applying the field-boundary approach to a broader set of physical features visible from space — training models capable of detecting roads, buildings, solar panels, trees, and more at global scale.
Benchmarks of The World
A shared, community-governed system for evaluating geospatial AI models — measuring real-world performance, failure modes, compute costs, and generalization across geographies and data types.