--- title: Core-S2L2A-249k canonical_url: "https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k" md_url: "https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k.md" repository: Major-TOM/Core-S2L2A-249k last_updated: 2026-05-21 license: cc-by-sa-4.0 storage_size: "328 GB" downloads: 14109 stars: 1 --- # Core-S2L2A-249k > Core-S2L2A-249k - Major-TOM 在 ModelScope 开源的数据集。Core-S2L2A-249k is a curated subset of the Major-TOM Core-S2L2A dataset, containing 248,719 Sentinel-2 L2A image patches uniformly sampled across the globe. It serves as the source imagery for pre-computed… Major-TOM/Core-S2L2A-249k 是 ModelScope 魔搭社区上的数据集，存储大小 328 GB，采用 cc-by-sa-4.0 许可。 - **Repository**: Major-TOM/Core-S2L2A-249k - **License**: cc-by-sa-4.0 - **Storage size**: 328 GB - **Downloads**: 14109 - **Stars**: 1 - **Last updated**: 2026-05-21 Source: https://www.modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k --- # Core-S2L2A-249k **Core-S2L2A-249k** is a curated subset of the [Major-TOM](https://github.com/ESA-PhiLab/MajorTOM) Core-S2L2A dataset, containing 248,719 Sentinel-2 L2A image patches uniformly sampled across the globe. It serves as the **source imagery** for pre-computed embedding datasets used by the [EarthEmbeddingExplorer](https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer) cross-modal retrieval web application. ## Overview | Property | Value | |----------|-------| | Source | Major-TOM Core-S2L2A | | Sensor | Sentinel-2 MSI (Level-2A) | | Number of patches | 248,719 | | Patch size | 384 × 384 pixels | | Spectral bands | 12 (B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12) | | Spatial resolution | 10 m / 20 m / 60 m (depending on band) | | Output format | GeoParquet | | License | CC-BY-SA-4.0 | ## Sampling Strategy This dataset was created by **uniform grid sampling** from the full Major-TOM Core-S2L2A archive: 1. A global grid is overlaid on the Major-TOM tile index. 2. For every **1/9 sampled grid cell**, the central bounding box is selected as the crop region. 3. Each crop is extracted at a fixed size of **384 × 384 pixels** to ensure consistency across all downstream embedding models. This strategy guarantees spatial diversity while keeping the dataset size manageable for large-scale embedding generation and interactive web retrieval. ## File Layout ``` . ├── metadata_249k.parquet # Metadata and geospatial index (248,719 rows) └── images_249k/ ├── part_00001.parquet # Image patches shard 1 ├── part_00002.parquet # Image patches shard 2 └── ... ``` ### Metadata Schema (`metadata_249k.parquet`) | Column | Type | Description | |--------|------|-------------| | `product_id` | string | Original Sentinel-2 product identifier | | `timestamp` | datetime | Acquisition timestamp | | `grid_cell` | string | Major-TOM grid cell identifier | | `grid_row_u` | int16 | Grid row index | | `grid_col_r` | int16 | Grid column index | | `geometry` | geometry | WGS-84 polygon (footprint) | | `centre_lat` | float32 | Latitude of patch centre | | `centre_lon` | float32 | Longitude of patch centre | | `utm_footprint` | string | Original UTM footprint as WKT | | `utm_crs` | string | UTM CRS (e.g. EPSG:32633) | | `pixel_bbox` | list | Pixel bounding box [x_min, y_min, x_max, y_max] | | `parquet_url` | string | Path to the image shard containing this patch | | `parquet_row` | int64 | Row index within the image shard | ### Image Patch Schema (`images_249k/part_*.parquet`) Each row contains a single 384 × 384 Sentinel-2 L2A patch stored as a 3-D array with shape `(384, 384, 12)` in uint16 format. The 12 bands follow the order: `[B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12]` ## Pre-computed Embedding Datasets The following embedding datasets were computed from **Core-S2L2A-249k** using various foundation models. All embeddings share the same 248,719 samples and geospatial metadata, enabling fair cross-model comparison. | Filename | Embedding Model | Crop Size | Model Input Size | Embedding Dim | Source | |----------|-----------------|-----------|------------------|---------------|--------| | `SigLIP_crop_384x384.parquet` | [SigLIP (ViT-SO400M-14-SigLIP-384)](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384) | 384×384 | 384×384 | 1152 | [Major-TOM/Core-S2RGB-249k-SigLIP](https://modelscope.cn/datasets/Major-TOM/Core-S2RGB-249k-SigLIP) | | `FarSLIP_crop_384x384.parquet` | [FarSLIP (ViT-B-16)](https://huggingface.co/ZhenShiL/FarSLIP) | 384×384 | 224×224 | 512 | [Major-TOM/Core-S2RGB-249k-FarSLIP](https://modelscope.cn/datasets/Major-TOM/Core-S2RGB-249k-FarSLIP) | | `DINOv2_crop_384x384.parquet` | [DINOv2-large](https://huggingface.co/facebook/dinov2-large) | 384×384 | 224×224 | 1024 | [Major-TOM/Core-S2RGB-249k-DINOv2](https://modelscope.cn/datasets/Major-TOM/Core-S2RGB-249k-DINOv2) | | `SatCLIP_crop_384x384.parquet` | [SatCLIP (ViT16-L40)](https://github.com/microsoft/satclip) | 384×384 | 224×224 | 256 | [Major-TOM/Core-S2L2A-249k-SatCLIP](https://modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-SatCLIP) | | `Clay_crop_384x384.parquet` | [Clay v1.5](https://github.com/Clay-foundation/model) | 384×384 | 384×384 | 1024 | [Major-TOM/Core-S2L2A-249k-Clay-v1_5](https://modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-Clay-v1_5) | | `OLMoEarth_Base_crop_384x384.parquet` | [OLMoEarth-Base](https://huggingface.co/allenai/OLMoEarth-Base-WS) | 384×384 | 128×128 | 768 | [Major-TOM/Core-S2L2A-249k-OlmoEarth](https://modelscope.cn/datasets/Major-TOM/Core-S2L2A-249k-OlmoEarth) | > **Note**: SigLIP, FarSLIP, and DINOv2 use RGB-only inputs (B04, B03, B02). SatCLIP, Clay, and OLMoEarth use multi-spectral inputs. ## Usage ```python import pandas as pd # Load metadata meta = pd.read_parquet("metadata_249k.parquet") print(len(meta), "patches") # Load image patches from a shard images = pd.read_parquet("images_249k/part_00001.parquet") print(images.iloc[0]["B04"].shape) # (384, 384) ``` ## Web Application Explore these embeddings interactively with the [EarthEmbeddingExplorer](https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer) web application, supporting text, image, and geolocation queries. ## Citation If you use this dataset, please cite both 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 and others}, 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.