Mapping a changing planet: TESSERA's global Earth observation embeddings now openly available on AWS

by Osho Jha, Dr. Clement Atzberger, and Chris Stoner, on 17 SEP 2026 in Amazon Simple Storage Service (S3), Announcements, Artificial Intelligence, Public Sector, Sustainability

Open data is reshaping how we understand and respond to global challenges. From climate change to food security to forest conservation, the ability to access and analyze large-scale geospatial data is critical for scientific research, policy-making, and real-world decision-making. Yet for most of the people who could benefit from it, satellite data has remained difficult to use—locked in petabytes of irregular, cloud-corrupted imagery that requires specialized expertise and significant computing resources to process.

Today, we are excited to announce that TESSERA, a global geospatial AI foundation model, is being made openly available through the Amazon Sustainability Data Initiative (ASDI) and the AWS Open Data Sponsorship Program. With compute and hosting support from Amazon Web Services (AWS), dClimate is completing TESSERA's global historical coverage and publishing the resulting analysis-ready data on Amazon Simple Storage Service (Amazon S3) for anyone to use, free of charge. In this post, we share what TESSERA is, how it works, and how researchers, builders, and institutions can start using it.

The challenge: turning raw pixels into usable insight

Two of the most valuable sources of Earth observation data are the European Space Agency's Sentinel-1 (radar) and Sentinel-2 (optical) missions, which image the planet continuously and freely. But working with this data directly is hard. A single year of Sentinel-2 imagery for one 100×100 km tile can exceed 100 GB, and analysts must correct for clouds, align observations over time, and build custom processing pipelines before they can answer even basic questions. These costs and complexities have kept advanced Earth observation in the hands of a small number of well-resourced organizations.

TESSERA was developed to remove that barrier. The Earth observation foundation model was developed by Arbol's Chief Scientist Dr. Clement Atzberger and researchers at the University of Cambridge. Building on that foundation, Arbol has served as an open source collaborator throughout TESSERA’s development, contributing applied research, engineering support, and real-world validation drawn from its climate risk underwriting. The work is described in a peer-reviewed publication and reflects years of research into how to represent the Earth's surface in a way that is both compact and broadly useful. dClimate's open source repository can be accessed here.

How TESSERA works

Rather than treating Earth observation as an image-processing problem, TESSERA takes a pixel-based approach focused on what matters most for environmental analysis: how the spectral signature of each location evolves over time. The model fuses Sentinel-1 and Sentinel-2 time series and encodes a full year of behavior for every 10-meter pixel on Earth into a compact, 128-dimensional "embedding"—a numerical fingerprint that captures the essential characteristics of that location and year.

Producing embeddings globally, across nine years, at 10-meter resolution makes TESSERA uniquely useful, with Google’s AlphaEarth among the only comparable efforts at this level of detail due to cost and difficulty.

These embeddings are analysis-ready. Instead of downloading and processing raw scenes, a user can retrieve a lightweight annual data layer and apply it directly to a downstream task with a small model on top—no fine-tuning of the underlying foundation model required. Because the embeddings are produced consistently across space and time, they can be compared from one year to the next and from one region to another, which is essential for tracking change.

The practical difference is significant. Downloading an annual embedding tile is far smaller and faster than retrieving the raw multi-sensor time series for the same area, which collapses the storage, bandwidth, and computing required to work at scale. Cambridge's open-source geotessera Python client, dClimate's usage README, and an open tile registry make the data straightforward to access and verify.

What the AWS Open Data Sponsorship Program enables

The AWS Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS, working with data providers to democratize access to data, lower the cost of working with it, and encourage communities to build on shared resources. For TESSERA, this support makes two things possible: completing a one-time global processing pass to finalize historical coverage, and hosting the resulting embeddings on Amazon S3 with requestor-friendly access so that anyone, anywhere, can use them without egress costs.

Completing the historical record matters because most environmental questions depend on consistent, multi-year comparison. Whether a forest is shrinking, whether a region's soils are gaining or losing carbon, or whether cropland is under stress can only be answered by looking across years. Making a global, comparable, analysis-ready archive openly available is a meaningful step toward democratizing that capability.

Real-world impact

TESSERA is already being used in the field. Arbol is using TESSERA foundation models to bring new climate insurance products to market in underserved regions where traditional insurers are retreating, and where limited data availability and modeling capability have long made these risks difficult or impossible to underwrite. By converting years of satellite observation into consistent, analysis-ready signals for every 10-meter patch of land, TESSERA gives underwriters the ground-level view needed to structure and price coverage for perils such as drought, flood, wildfire, and crop failure in markets that have historically gone unprotected.

With open access, we expect the range of applications to grow well beyond what any single organization could build. Some of the sectors likely to benefit first include:

• Climate and conservation research, for monitoring deforestation, biodiversity, and ecosystem change—including in regions that have historically lacked consistent observation.
• Agriculture and food, for assessing soil health, crop condition, and yield without relying solely on costly fieldwork.
• Carbon and restoration, for verifying project outcomes with independent, high-resolution evidence.
• Insurance, lending, and the public sector, for understanding land and natural-risk exposure with greater clarity.

These embeddings also power CYCLOPS, Arbol’s land and nature intelligence unit led by Dr. Atzberger, which applies TESSERA to soil health, biomass, yield, and land-use analysis: an example of how an open foundation can support both public research and applied tools.

Get started

TESSERA's global embeddings are available through the Registry of Open Data on AWS. You can access the data on Amazon S3 using Cambridge's open-source geotessera Python client and learn more in the dClimate readme, explore the tile registry for reproducibility and integrity verification, and join the growing community of researchers and developers building on open Earth observation data. To learn more about the Amazon Sustainability Data Initiative, visit https://sustainabilityexchange.amazon.com/, and to learn more about AWS Open Data, visit https://opendata.aws/.

We're excited to see what the community builds. Open, analysis-ready Earth data has the potential to do for environmental science what open data has already done for fields like genomics and astronomy—turn a specialized, resource-intensive discipline into a shared platform that anyone can build on.

"The bottleneck in Earth observation has never been satellites — it's been the cost and complexity of turning raw imagery into something a scientist or developer can actually use. TESSERA clears that bottleneck. By making a decade of analysis-ready, 10-meter embeddings freely available on AWS, we're collapsing the infrastructure burden that has kept advanced Earth observation out of reach for most organizations. We're proud to host it."
— Chris Stoner, Geospatial Lead, AWS Open Data