Every second, satellites and sensors generate terabytes of data about Earth. That ocean of information holds concrete answers: where forests are disappearing, which crops are at risk, and how ecosystems change over time.
OlmoEarth Platform comes to remove the barriers that have kept those insights out of reach for small teams or organizations without large budgets. Ai2 doesn’t just offer a model — it provides an open, end-to-end infrastructure to turn satellite images and geospatial data into practical decisions.
What is OlmoEarth Platform and why it matters
OlmoEarth Platform is an integrated end-to-end solution covering data collection, annotation, training, inference and deployment. At its core is the foundation model family called OlmoEarth, pretrained on roughly 10 terabytes of land observations.
Technically, the key idea is multitask versatility. Instead of training separate models for crop mapping, deforestation or land-use classification, a single foundation model can adapt to many applications with fine tuning. That reduces cost, development time and the need for specialized engineering teams.
OlmoEarth aims to be a reusable foundation: fewer models to maintain, more applications built on the same pretrained backbone.
Main components
OlmoEarth Studio
This is the free workspace for building custom models. You can upload labeled data, create annotation tasks and coordinate teams. It also automates image acquisition from sources like Sentinel-1, Sentinel-2 and Landsat based on your area of interest and time range.
Studio makes fine tuning easy with predefined recipes or custom settings. You can iterate fast: check predictions, add labels and retrain without leaving the tool.
OlmoEarth Viewer
A web app to explore the maps generated by the models. Standout features:
- Side-by-side comparison, swipe bar and transparency to spot changes.
- Temporal slider to follow evolution on specific dates.
- Instant analytics that show class breakdowns and confidence levels.
- Controlled publishing from Studio with options: public, restricted link, or internal use.
OlmoEarth Run
A workflow engine for reproducible work at scale. A workflow is split into parallel tasks where each step declares code, inputs, outputs and required resources (for example CPU or GPU). Run handles scheduling, resource allocation, automatic retries and per-task tracking.
This improves reproducibility and makes it simple to run jobs on specific hardware or across cloud regions without vendor lock-in.
APIs and example projects
The platform exposes APIs to integrate key steps: import datasets in common geospatial formats, launch and monitor inference jobs, and download predictions to use in other systems.
Additionally, OlmoEarth Projects publishes examples and guides to train custom models using the pretrained weights. You can run those models offline and serve them locally if your project requires it.
A deeper technical look
- Data: pretrained on ~10 TB of land observations, combining optical and radar to be robust against clouds and varied conditions.
- Architecture: a foundation family capable of multitasking, optimized for efficient fine tuning and deployment. This speeds up time to useful results compared to training from scratch.
- Orchestration:
OlmoEarth Runuses task partitioning and dispatch to worker nodes, with per-task traceability and declarative resource control. - Reproducibility: every workflow declares code and I/O, making it easier to rerun pipelines and audit outputs.
If you work in ecosystems with limited field data, local fine tuning helps the model adapt to regional conditions without requiring huge annotation volumes.
Use cases and field impact
OlmoEarth Platform is already used with partners for fire monitoring, food security, conservation and ecosystem monitoring.
Concrete examples:
- With JPL they estimate live fuel moisture by combining radar and optical data, helping prioritize fire prevention and response.
- In Nandi County, Kenya, IFPRI updated crop maps more frequently than the traditional five-year cycle, supporting seed and fertilizer decisions.
- Global Mangrove Watch achieved more frequent updates with less annotation effort, improving near-real-time response to losses.
The practical advantage is clear: organizations with modest budgets can keep frequent updates because the platform lowers costs and the need for large technical teams.
What it means for developers and scientists
If you’re an engineer or data scientist, OlmoEarth offers:
- Pretrained weights as a starting point for fine tuning.
- Reproducible, controllable pipelines with
OlmoEarth Run. - APIs to integrate inference into production systems.
- Public repo examples to get started quickly and run offline if needed.
If you manage public policy or a conservation project, the platform lets you get updated maps and actionable visuals without depending on a big in-house infrastructure.
Final reflection
OlmoEarth Platform seeks to democratize Earth intelligence by offering not only high-performance models, but the infrastructure for those models to produce real impact. The mix of an efficient foundation model, collaborative tools and reproducible pipelines lowers the barrier for governments, NGOs and small companies to turn satellite data into decisions.
The challenge now is community and adoption. The more teams contribute data, validations and use cases, the stronger the base will be to tackle urgent problems like deforestation, food security and fire resilience.
