rs-embed 0.2.0

Unified ROI->embedding interface for remote sensing foundation models.

rs-embed

One line code to get Any Remote Sensing Foundation Model (RSFM) embeddings for Any Place and Any Time

Docs · StartNow · Releases · Changelog · UseCase · Paper

Get Start on I-GUIDE Today!

TL;DR

emb = get_embedding("prithvi", spatial=..., temporal=..., output=...)

Install

# base install (always use the latest version for best experience)
pip install --upgrade rs-embed

# add [terratorch] only if you use terramind
pip install --upgrade "rs-embed[terratorch]"

For local development:

git clone https://github.com/cybergis/rs-embed.git
cd rs-embed
pip install -e .  # use -e ".[terratorch]" if you need terramind

If this is your first time using Google Earth Engine, authenticate once:

earthengine authenticate

Quick Example

from rs_embed import PointBuffer, TemporalSpec, OutputSpec, get_embedding

spatial = PointBuffer(lon=121.5, lat=31.2, buffer_m=2048)
temporal = TemporalSpec.range(
    "2022-06-01",
    "2022-09-01",
)

emb = get_embedding(
    "prithvi",
    spatial=spatial,
    temporal=temporal,
    output=OutputSpec.pooled(),
)

Tip: Default settings are designed as a trade-off between compute cost and embedding quality. If you have sufficient compute resources, check Choosing Settings and individual model pages to get the best results.

See the visualization helper and end-to-end notebook in the repository:

• examples/plot_utils.py
• examples/playground.ipynb

Main API

For new users, start with these primary APIs:

• get_embedding(...): one ROI -> one embedding
• get_embeddings_batch(...): many ROIs, same model
• export_batch(...): export datasets / experiments (single or multiple ROIs)
• inspect_provider_patch(...): inspect raw provider patches before inference

Supported Models

This is a convenience index with basic model info only (for quick scanning / links). For detailed I/O behavior and preprocessing notes, see Supported Models.

Precomputed Embeddings

Model ID	Resolution	Time Coverage	Publication
tessera	10m	2017-2025	CVPR 2026
gse (Alpha Earth)	10 m	2017-2024	arXiv 2025
copernicus	0.25°	2021	ICCV 2025

On-the-fly Foundation Models

Model ID	Primary Input	Resolution(Default)	Publication	Link
olmoearth	S2 L2A 12-band / S1 VV-VH	10m	CVPR 2026	link
agrifm	S2 time series (10-band)	10m	RSE 2026	link
thor	S2 10-band	10m	arXiv 2026	link
terrafm	S2 12-band / S1 VV-VH	10m	ICLR 2026	link
terramind	S2 12-band	10m	ICCV 2025	link
galileo	S2 time series (10-band)	10m	ICML 2025	link
wildsat	S2 RGB	10m	ICCV 2025	link
anysat	S2 time series (10-band)	10m	CVPR 2025	link
fomo	S2 12-band	10m	AAAI 2025	link
prithvi	S2 6-band	30m	arXiv 2024	link
satvision	TOA 14-channel	1000m	arXiv 2024	link
dofa	Multi-band + wavelengths	10m	arXiv 2024	link
clay	S2 L2A 10-band (+ latlon/time/gsd/wavelengths)	10m	model release v1.5, 2024	link
satmaepp	S2 RGB (modality=rgb, default) or S2 SR 10-band (modality=s2_10b)	10m	CVPR 2024	link
remoteclip	S2 RGB	10m	TGRS 2024	link
scalemae	S2 RGB (+ scale)	10m	ICCV 2023	link
satmae	S2 RGB	10m	NeurIPS 2022	link

Resolution here means the default provider/source fetch resolution used by the adapter, not the final resized tensor shape seen by the model.

Learn More

📚 Full documentation

🪄 Get Started: Try rs-embed Now

🪀 Use case: Maize yield mapping Illinois

📢 Disscusion

🧾 Release policy and versioning

📌 Project changelog

Extending & Contributing

We welcome issues for new model integrations, extension ideas, bugs, and documentation gaps. If you have your own work, or a model or paper that you think would be valuable to include in rs-embed, please open an Issue and share the relevant links, context, and examples.

We also warmly welcome community contributions, including new model support, bug fixes, documentation improvements, and example notebooks. If you would like to contribute directly, please start with the extending guide and the contributing guide.

🎖 Acknowledgements

We would like to thank the following organizations and projects that make rs-embed possible: Google Earth Engine, TorchGeo, GeoTessera, TerraTorch, rshf, and the Copernicus-Embed.

This library also builds upon the incredible work of the Remote Sensing community!(Full list and citations available in our Documentation)

Citation

@article{ye2026modelplacetimeremote,
      title={Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand},
      author={Dingqi Ye and Daniel Kiv and Wei Hu and Jimeng Shi and Shaowen Wang},
      year={2026},
      eprint={2602.23678},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.23678},
}

License

This project is released under the Apache-2.0

Contributors