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Fields of the World

Expanding the world's first open ecosystem for global agricultural field boundary detection from satellite imagery — combining a global benchmark dataset, baseline ML models, inference tools, and web applications.

Aerial view of a rural landscape with colorful patchwork fields, a winding road, scattered trees, and distant hills under a partly cloudy sky.

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.

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Research Partners
Kerner Lab, Arizona State University
Multimodal Vision Research Lab, WashU
Ai2
Microsoft AI for Good Lab
Related
Press Release

Key Project Characteristics

01
Global Coverage

Comprehensive field boundaries across the globe

02
Open Dataset

Freely accessible to researchers and innovators worldwide

03
AI-Powered

Created using advanced machine learning and satellite imagery

04
In-Browser Analysis

You can run AI models directly in your web browser. No coding or software setup is required to generate field boundaries.

05
Agricultural Tracking

Supports food security, crop types, and land cover / land use assessments.

06
Custom AI Development

Developers can download the benchmark dataset from Source Cooperative to train custom geospatial AI models.

Research Focus

01
Benchmark Datasets

AI-ready datasets for training and evaluating field boundary segmentation models, covering vastly different agricultural landscapes across the globe.

02
Model Training and Evaluation

Off-the-shelf field boundary segmentation models and code for custom training and evaluation. Evaluation metrics that reflect real-world deployment conditions.

03
Global Inference

Global, multi-year field boundary maps created using FTW-trained models and global satellite mosaics, providing field boundary polygons anywhere in the world.

04
Open Source & Cloud Native

All data, models, and code are published freely with permissible licenses on public platforms in cloud-native geospatial formats.

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.

Our Partners

We identify technical fellows right from the community contributing to our work. See below for ways to get involved, or contact us to join the experts providing input and guidance as work progresses.

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.

User Advisory Group

The Fields of The World User Advisory Group will serve as a standing body of practitioners who ensure that the initiative is designed with users rather than for users.

The group will provide continuous feedback on data products, workflows, documentation, and research priorities while serving as early pilot partners for new datasets and tools. Modeled after the human-centered design principles, the Advisory Group will emphasize real-world application, iterative improvement, and diverse perspectives from organizations using geospatial data to make decisions.

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.

Explore FTW

Dig in to the full ecosystem behind Fields of The World.

Jump in
Find Us on GitHub

Contribute to our open source ecosystem.

GitHub
Community Discussions

Join the lively group of experts discussing FTW on Slack

Coming soon
User Advisory Group

Contact us to join the experts providing input and guidance as work progresses.

Contact us
Technical Community Meetings

Join the Google Group to receive invitations to regular meetings.

Join now
Access all of the FTW Data

Access all of the data through source.coop.

See the data
A world map shows land areas colored in purple and green, highlighting regions across all continents except Antarctica; major continents and oceans are labeled in white text.

Share Your Ideas

Does your organization have a use case for field boundary data? We’d love to learn about your real-world application so it can guide our work.

Tell Us More

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Other Inititatives

Aerial view of a densely packed neighborhood with red-roofed houses, green trees, and narrow roads winding through the area.

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.

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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.