Wherobots and Taylor Geospatial Engine Bring Fields-of-the-World Models to Production Scale

Agriculture depends on timely, reliable insight into what’s happening on the ground—what’s being planted, what’s being harvested, and how fields evolve over time. The Fields of The World (FTW) project was created to support exactly this mission, by building a fully open ecosystem of labeled data, software, standards and models to create a reliable global map of agricultural field boundaries using AI and Earth Observation (EO) data.

Over the past several months, Wherobots has been working closely with the Taylor Geospatial Engine (TGE) team driving the FTW project to turn high-performing research models into operational, production-scale data products. This collaboration builds on TGE’s broader effort to accelerate AI & EO development through connecting cutting edge research to real world needs. Learn more about FTW at fieldsofthe.world and about TGE’s agricultural AI initiatives here.

Turning Fields-of-the-World Research Models Into Operational Pipelines

FTW Phase 2 surfaced state-of-the-art computer vision models for interpreting Sentinel-2 time-series imagery and predicting locations of agricultural fields. But, research models alone aren’t enough to deliver real agricultural insight. They must be reproducible at scale, compute-efficient, and aligned with downstream applications.

This is where Wherobots focused its efforts: optimizing inference pipelines, distributing computation efficiently, and generating data products that can serve as reliable foundations for agricultural modeling, monitoring, and analysis.

TGE’s objective was simple: transform breakthrough research into something developers, end-users, and organizations can actually use.

"This project is a testament to what happens when the academic community, nonprofits, and industry all pull in the same direction… Wherobots' ability to run these open-source models at scale makes it possible for the community's work to reach a global audience, and open access to predictions and mosaics ensures that more researchers and innovators can build on top of it."

Jennifer Marcus

Executive Director, Taylor Geospatial Engine

Open-Sourced Seasonal Mosaics and Model Predictions

Today, we’re releasing the first production-scale outputs from this collaboration:

Sentinel-2 Seasonal Mosaics

Cloud-free, analysis-ready mosaics tailored to key agricultural seasons—planting and harvest—built from Sentinel-2 imagery. These mosaics provide a consistent, high-quality foundation for model training, monitoring workflows, and large-scale geospatial analysis.

FTW Phase 2 Model Predictions

Per-pixel prediction outputs generated by running the top-performing FTW Phase 2 model across these seasonal mosaics, aligned with FTW’s agricultural labeling standards.

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This is an example output of the field boundary model in a large scale AOI in Japan and Mexico. The comparison is between the 2023 predictions and 2023 + 2024 in predictions (2024 in bright green).

Both datasets are openly available on Source Cooperative: https://source.coop/wherobots/fields-of-the-world

These resources are designed to make advanced agricultural AI more accessible—supporting innovation in food security, sustainability, and data-driven farming.

Enabled by the Spatial Intelligence Cloud

This release also highlights the underlying engine that made it possible. The Wherobots Spatial Intelligence Cloud is built specifically for large-scale geospatial machine learning workflows—constructing analysis-ready mosaics, executing distributed model inference, and writing results into modern, cloud-native formats like Zarr with exceptional efficiency. And model outputs can be further processed and analyzed within WherobotsDB, using the power of Apache Sedona to refine the field geometries or calculate vegetative indices at scale. These capabilities are part of our suite of tools within our AI for Earth product area.

Under the hood, the platform uses state-of-the-art tooling for raster processing, GPU-accelerated inference, chunk-aligned storage, and aggressive cost optimization. These choices allow us to run continental-scale pipelines at speeds and costs that would have been unimaginable even a few years ago.

As agricultural AI models continue to grow in scope and resolution, this kind of infrastructure becomes essential. Our goal is to make it straightforward for teams to experiment, scale, and operationalize geospatial ML without needing to reinvent the entire data stack.

One of the most important parts about what the Wherobots’ team has done is to make it easy to see where and how the model is failing. For example, the model's poor performance in Nevada and the tiling artifacts from the previous model runs led to important changes in how we should be training models.

Nathan Jacobs

Assistant Vice Provost for Digital Transformation, Washington University in St. Louis

What’s Next

This is just the beginning. We’re continuing to work with the FTW and TGE teams to expand coverage, operationalize more models, and build richer analysis-ready layers for the agricultural AI ecosystem.

If you’re exploring geospatial ML, agricultural monitoring, or large-scale satellite data processing, we’re excited to see what you build with these new open datasets—and with the Wherobots AI for Earth capabilities in your pipeline.

Getting in Touch about Wherobots AI for Earth

If you would like to continue to stay up to date about the Wherobots AI for Earth capabilities for solving real world problems, or speak to the Wherobots team about utilizing these capabilities in production, please reach out to us here.