| --- |
| license: c-uda |
| pretty_name: MillionST |
| viewer: false |
| tags: |
| - image |
| - geospatial |
| - timeseries |
| - remote-sensing |
| - earth-observation |
| - satellite-image-time-series |
| - spatiotemporal |
| - self-supervised-learning |
| - masked-image-modeling |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # MillionST |
|
|
| MillionST is a large-scale satellite image time series dataset curated for pre-training spatiotemporal foundation models for Earth observation. |
|
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| The dataset contains approximately 1 million satellite images from 100,000 geographic locations, with each location observed across 10 temporal phases over five years. It is designed to capture diverse geospatial changes, seasonal variations, and long-term land-surface dynamics. |
|
|
| MillionST was introduced in the paper: |
|
|
| **TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series** |
| https://arxiv.org/abs/2505.08723 |
|
|
| Code is available at: https://github.com/MiliLab/TiMo |
|
|
| ## Dataset Details |
|
|
| - **Dataset name:** MillionST |
| - **Dataset type:** Satellite image time series |
| - **Domain:** Remote sensing / Earth observation |
| - **Scale:** Approximately 1 million images |
| - **Geographic locations:** 100,000 locations |
| - **Temporal phases:** 10 phases |
| - **Temporal span:** Five years |
| - **Associated model:** TiMo |
| - **Primary purpose:** Self-supervised pre-training for spatiotemporal representation learning |
|
|
| ## Data Access |
|
|
| This dataset is released for computational research use. Users should follow the license and any access terms shown on this page. |
|
|
| If you use MillionST in your research, please cite the associated paper. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{qin2025timo, |
| title={TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series}, |
| author={Qin, Xiaolei and Wang, Di and Zhang, Jing and Wang, Fengxiang and Su, Xin and Du, Bo and Zhang, Liangpei}, |
| journal={arXiv preprint arXiv:2505.08723}, |
| year={2025} |
| } |
