According to a recent LinkedIn post from Earthmover, the company’s data marketplace now features Taylor Geospatial’s global agricultural field boundary dataset, “Fields of the World.” The post indicates this is Taylor Geospatial’s third dataset on the platform, positioned alongside global Sentinel‑2 median mosaics optimized for large‑scale, analysis‑ready Earth observation workloads.
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The LinkedIn post further highlights availability of the AlphaEarth Foundations dataset, which provides Google DeepMind satellite embeddings from 2017 to 2025 to support AI model development. Together with the new Fields of the World predictions for 2024–2025, the marketplace is presented as offering inputs, embeddings, and predictions in cloud‑native formats such as Zarr V3, GeoParquet, and PMTiles.
According to the post, these datasets are the result of collaborations involving researchers from Arizona State University, Washington University in St. Louis, Clark University, and Microsoft’s AI for Good Lab, among others. The effort reportedly leverages Wherobots RasterFlow for global inference and is intended to address challenges in modeling diverse agricultural landscapes, from smallholder plots in Ethiopia to large farms in the U.S. and Brazil.
For investors, the post suggests that Earthmover is expanding its role as an infrastructure provider at the intersection of Earth observation, agriculture, and AI. If adoption grows among agritech firms, insurers, commodity traders, and climate‑risk analysts, this expanded dataset catalog could enhance the platform’s monetization potential and strengthen its competitive positioning in geospatial data marketplaces.
The emphasis on cloud‑native, scalable data formats may also reduce integration friction for enterprise users, potentially supporting higher usage and stickiness. At the same time, the niche and technically demanding nature of global agricultural AI means actual revenue impact will depend on how effectively Earthmover can convert technical partnerships and research collaborations into recurring commercial demand.

