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Earth system foundation model data, training, and eval
| Date | Stars |
|---|---|
| 2026-07-31 | 289 |
| 2026-08-05 | 292 |
| 2026-08-06 | 292 |
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<div align="center">
<img src="https://raw.githubusercontent.com/allenai/olmoearth_pretrain/main/assets/OlmoEarth-logo.png" alt="OlmoEarth Logo" style="width: 600px; margin-left:'auto' margin-right:'auto' display:'block'"/>
<br>
<br>
</div>
<p align="center">
<a href="https://github.com/allenai/olmoearth_pretrain/blob/main/LICENSE">
<img alt="GitHub License" src="https://img.shields.io/badge/license-OlmoEarth-green">
</a>
<a href="https://huggingface.co/collections/allenai/olmoearth">
<img alt="Model Checkpoints" src="https://img.shields.io/badge/%F0%9F%A4%97%20HF-Models-yellow">
</a>
<a href="https://allenai.org/papers/olmoearth">
<img alt="Paper PDF" src="https://img.shields.io/badge/OlmoEarth-pdf-blue">
</a>
</p>
The OlmoEarth models are a flexible, multi-modal, spatio-temporal family of foundation models for Earth Observations.
The OlmoEarth models exist as part of the [OlmoEarth platform](https://olmoearth.allenai.org/). The OlmoEarth Platform is an end-to-end solution for scalable planetary intelligence, providing everything needed to go from raw data through R&D, to fine-tuning and production deployment.
## Installation
We recommend Python 3.12, and recommend using [uv](https://docs.astral.sh/uv/getting-started/installation/).
To install dependencies with uv, run:
```bash
git clone [email protected]:allenai/olmoearth_pretrain.git
cd olmoearth_pretrain
uv sync --locked --all-extras --python 3.12
# only necessary for development
uv tool install pre-commit --with pre-commit-uv --force-reinstall
```
uv installs everything into a venv, so to keep using python commands you can activate uv's venv: `source .venv/bin/activate`. Otherwise, swap to `uv run python`.
### Inference-Only Installation
For inference and model loading without training dependencies:
```bash
uv sync --locked
```
OlmoEarth is built using [OLMo-core](https://github.com/allenai/OLMo-core.git). OLMo-core's published [Docker images](https://github.com/orgs/allenai/packages?repo_name=OLMo-core) contain all core and optional dependencies.
## Model Summary
<img src="https://raw.githubusercontent.com/allenai/olmoearth_pretrain/main/assets/model.png" alt="Model Architecture Diagram" style="width: 800px; margin-left:'auto' margin-right:'auto' display:'block'"/>
The OlmoEarth models are trained on three satellite modalities (Sentinel 2, Sentinel 1 and Landsat) and six derived maps (OpenStreetMap, WorldCover, USDA Cropland Data Layer, SRTM DEM, WRI Canopy Height Map, and WorldCereal).
We iteratively release improvements to our OlmoEarth models. These are recorded in our [changelog](https://docs.olmoearth.allenai.org/changelog).
<details>
<summary>v1 </summary>
The v1 models were trained using the scripts in [`scripts/official/v1`](scripts/official/v1/). We describe this model in the [original OlmoEarth report](arxiv.org/abs/2511.13655).
| Model Size | Weights | Encoder Params | Decoder Params |
| --- | --- | --- | --- |
| Nano | [link](https://huggingface.co/allenai/OlmoEarth-v1-Nano) | 1.4M | 800K |
| Tiny | [link](https://huggingface.co/allenai/OlmoEarth-v1-Tiny) | 6.2M | 1.9M |
| Base | [link](https://huggingface.co/allenai/OlmoEarth-v1-Base) | 89M | 30M |
| Large | [link](https://huggingface.co/allenai/OlmoEarth-v1-Large) | 308M | 53M |
</details>
<details>
<summary> v1.1 </summary>
The v1.1 models were trained using the scripts in [`scripts/official/v1_1`](scripts/official/v1_1/). We describe this model in the [OlmoEarth v1.1 tech report](https://arxiv.org/abs/2605.20804v1).
| Model Size | Weights | Encoder Params | Decoder Params |
| --- | --- | --- | --- |
| Nano | [link](https://huggingface.co/allenai/OlmoEarth-v1_1-Nano) | 1.7M | 800K |
| Tiny | [link](https://huggingface.co/allenai/OlmoEarth-v1_1-Tiny) | 12.5M | 1.9M |
| Base | [link](https://huggingface.co/allenai/OlmoEarth-v1_1-Base) | 114M | 30M |
</details>
<details open>
<summary> v1.2 </summary>
The v1.2 models were trained using the scripts in [`scriptsExcerpt of 7,211 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d28c274a50004289, llm:Repository description: 'Earth system foundation model data, training, and eval' (allenai/olmoearth_pretrain).
matched fp:d28c274a50004289, llm:Repository description: 'Earth system foundation model data, training, and eval' (allenai/olmoearth_pretrain).
matched fp:d28c274a50004289, llm:Repository description: 'Earth system foundation model data, training, and eval' (allenai/olmoearth_pretrain).