# Earth observation insights

Rangoon Insights reporting and guides tagged Earth observation.

Canonical: https://rangoon.ai/insights/tags/earth-observation/

## Topics
- [Agent systems](https://rangoon.ai/insights/topics/agents/): Agent runtimes, handoffs, tools, and the boundaries that make them reviewable.
- [Governance](https://rangoon.ai/insights/topics/governance/): Policy, identity, authorization, approvals, and evidence for consequential work.
- [Infrastructure](https://rangoon.ai/insights/topics/infrastructure/): Model serving, sandboxes, tool services, and deployment boundaries.
- [Models](https://rangoon.ai/insights/topics/models/): Inference choices, local serving, compatibility, and operational tradeoffs.

## Stories
- [Ai2 introduced the OlmoEarth platform for open Earth-observation models and planetary-analysis tooling](https://rangoon.ai/insights/archive-ai2-olmoearth-platform-november-2025/)
  Ai2 introduced the OlmoEarth platform on November 4, 2025, pairing open Earth-observation model work with tools intended for planetary insights.
- [ESA and IBM released TerraMind as a multimodal Earth-observation foundation model](https://rangoon.ai/insights/archive-esa-ibm-terramind-earth-observation-april-2025/)
  ESA and IBM released the first TerraMind version on April 22, 2025, presenting a multimodal model for combining several types of Earth-observation data.
- [AlphaEarth Foundations framed mapping as reusable representation, not ground truth](https://rangoon.ai/insights/archive-alphaearth-foundations-geospatial-embeddings/)
  Google DeepMind introduced AlphaEarth Foundations in July 2025 to create geospatial embeddings from diverse observations, supporting mapping tasks with label and validation limits.
- [Prithvi-EO-2.0 expanded geospatial representation without becoming ground truth](https://rangoon.ai/insights/archive-prithvi-eo-2-global-geospatial/)
  NASA, IBM and Jülich released Prithvi-EO-2.0 in December 2024, extending an Earth-observation foundation model with global multi-temporal satellite data.


## More information

- [Documentation index](https://rangoon.ai/llms.txt)
- [AI access and policies](https://rangoon.ai/ai/)
- [Source repository](https://github.com/hypler-dev/rangoon)