---
title: Core-S2L2A-249k-Clay-v1_5
canonical_url: "https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-Clay-v1_5"
md_url: "https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-Clay-v1_5.md"
repository: Major-TOM/Core-S2L2A-249k-Clay-v1_5
last_updated: 2026-08-28
license: cc-by-sa-4.0
storage_size: "1.0 GB"
downloads: 86
stars: 0
---

# Core-S2L2A-249k-Clay-v1_5

> Core-S2L2A-249k-Clay-v1_5 - Major-TOM 在 ModelScope 开源的数据集。Core-S2L2A-249k-Clay-v1.5

Major-TOM/Core-S2L2A-249k-Clay-v1_5 是 ModelScope 魔搭社区上的数据集，存储大小 1.0 GB，采用 cc-by-sa-4.0 许可。

- **Repository**: Major-TOM/Core-S2L2A-249k-Clay-v1_5
- **License**: cc-by-sa-4.0
- **Storage size**: 1.0 GB
- **Downloads**: 86
- **Stars**: 0
- **Last updated**: 2026-08-28

Source: https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-Clay-v1_5

---

# Core-S2L2A-249k-Clay-v1.5

This dataset contains **pre-computed image embeddings** for the [Core-S2L2A-249k](https://modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k) satellite imagery subset, generated using the **[Clay v1.5](https://github.com/Clay-foundation/model)** foundation model.

## What is Clay?

Clay is an open-source foundation model for Earth observation. It uses a **Masked Autoencoder (MAE)** architecture with dynamic patch embeddings conditioned on sensor wavelengths. The model was trained on multi-spectral imagery from Sentinel-2, Landsat, NAIP, and other sensors.

- **Model**: Clay v1.5 (large)
- **Architecture**: ViT-Large MAE with dynamic embeddings
- **Embedding dimension**: 1024
- **Input**: 10 Sentinel-2 L2A bands (B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12)
- **Patch size**: 8
- **Input size**: 384 × 384

## How the embeddings were generated

1. **Source imagery**: MajorTOM Core-S2L2A-249k (~249k Sentinel-2 L2A chips, 384×384 pixels).
2. **Preprocessing**: Each chip was normalized using Clay's Sentinel-2 mean and std statistics.
3. **Spatiotemporal inputs**: The metadata-aware file conditions Clay on the acquisition time from
   `product_datetime` and the WGS84 centroid derived from each GeoTIFF's CRS and bounds.
4. **Inference**: The Clay encoder (without masking) was used to extract the CLS-token embedding from each chip.
5. **Output**: One 1024-dimensional embedding vector per chip, together with spatial and input-audit metadata.

Clay's metadata inputs use the official cyclical encodings:

- Time: `[sin(2π week/52), cos(2π week/52), sin(2π hour/24), cos(2π hour/24)]`.
- Location: `[sin(latitude), cos(latitude), sin(longitude), cos(longitude)]`, with angles in radians.

If a field cannot be recovered, only that four-value input is replaced with zeros. The source columns described
below make this fallback auditable.

The generation script is `generate_embeddings.py` from the [EarthEmbeddingExplorer](https://github.com/OpenGeoScope/EarthEmbeddingExplorer) repository.

## Dataset structure

The dataset provides two GeoParquet variants:

```
Clay_crop_384x384.parquet
Clay_crop_384x384_with_space_time_input.parquet
```

| File | Clay metadata input | Purpose |
| :--- | :--- | :--- |
| `Clay_crop_384x384.parquet` | Zero time and location vectors | Original visual-only index retained for reproducibility |
| `Clay_crop_384x384_with_space_time_input.parquet` | Real acquisition time and GeoTIFF footprint centroid | Recommended index for EarthEmbeddingExplorer |

### Metadata-aware file integrity

- Rows: `248,719`
- Size: `1,083,918,437` bytes
- SHA-256: `658575606e327ef3e760b45682773aee4369a754d6e6abc8cd49f6e46ba3c92e`
- Time source: `parquet_product_datetime` for all rows
- Location source: `tiff_bounds` for all rows

### Columns

| Column | Type | Description |
| :--- | :--- | :--- |
| `unique_id` | string | SHA-256 checksum of geometry + timestamp + product_id + embedding |
| `embedding` | float32[1024] | Clay v1.5 embedding vector |
| `timestamp` | string | Acquisition time (e.g., `20221115T161819`) |
| `product_id` | string | Unique scene identifier |
| `grid_cell` | string | MajorTOM hierarchical grid code |
| `grid_row_u` | int16 | Grid row index |
| `grid_col_r` | int16 | Grid column index |
| `geometry` | geometry | WGS84 polygon of the chip footprint |
| `centre_lat` | float32 | Center latitude |
| `centre_lon` | float32 | Center longitude |
| `utm_footprint` | string | UTM footprint WKT |
| `utm_crs` | string | UTM CRS string |
| `pixel_bbox` | list | Pixel bounding box `[x, y, x+w, y+h]` |
| `parquet_row` | int64 | Row index in the source imagery Parquet shard |
| `parquet_url` | string | URL to the source imagery Parquet shard |
| `clay_time_input` | float32[4] | Time vector passed to Clay |
| `clay_latlon_input` | float32[4] | Latitude/longitude vector passed to Clay |
| `clay_time_input_source` | string | `tiff_tag`, `parquet_product_datetime`, `embedding_timestamp`, or `missing_zero_fallback` |
| `clay_latlon_input_source` | string | `tiff_bounds`, `embedding_center`, or `missing_zero_fallback` |

The four `clay_*` audit columns are present in the metadata-aware file only. Source precedence is TIFF time tag,
source `product_datetime`, then embedding timestamp for time; TIFF bounds, then embedding center for location.

## Usage

You can load the embeddings directly with pandas or geopandas:

```python
import pandas as pd

df = pd.read_parquet("Clay_crop_384x384_with_space_time_input.parquet")
embeddings = df["embedding"].tolist()  # List of 1024-dim vectors
```

For cross-modal retrieval, pair this dataset with the [EarthEmbeddingExplorer](https://github.com/OpenGeoScope/EarthEmbeddingExplorer) web application.

## Acknowledgements

- [Clay Foundation Model](https://github.com/Clay-foundation/model) — Development Seed
- [MajorTOM](https://github.com/ESA-PhiLab/MajorTOM) — European Space Agency

## Citation

If you use this embedding dataset, please cite the EarthEmbeddingExplorer tutorial paper and the original Major-TOM paper:

```bibtex
@article{zheng2026earthembeddingexplorer,
  title={EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images},
  author={Zheng, Yijie and Wu, Weijie and Wu, Bingyue and Zhao, Long and Li, Guoqing and Czerkawski, Mikolaj and Klemmer, Konstantin},
  journal={arXiv preprint arXiv:2603.29441},
  year={2026},
  note={ICLR 2026 Workshop ML4RS Tutorial Track (oral)}
}
```

```bibtex
@inproceedings{francis2024majortom,
  title={Major TOM: Expandable Datasets for Earth Observation},
  author={Francis, Alistair and Czerkawski, Mikolaj},
  year={2024},
  booktitle={IGARSS 2024},
  eprint={2402.12095},
  archivePrefix={arXiv}
}
```

## License

This dataset is released under the [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.