| --- |
| license: cc-by-sa-4.0 |
| size_categories: |
| - 10M<n<100M |
| task_categories: |
| - image-feature-extraction |
| tags: |
| - embeddings |
| - earth-observation |
| - remote-sensing |
| - sentinel-2 |
| - satellite |
| - geospatial |
| - satellite-imagery |
| configs: |
| - config_name: default |
| data_files: embeddings/*.parquet |
| --- |
| |
|  |
|
|
| # Core-S2RGB-DINOv2 🔴🟢🔵 |
|
|
| [**Project Page (EarthEmbeddingExplorer)**](https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer) | [**GitHub**](https://github.com/ESA-PhiLab/Major-TOM) | [**Paper (Tutorial)**](https://huggingface.co/papers/2603.29441) | [**Paper (Dataset)**](https://huggingface.co/papers/2412.05600) |
|
|
| | Dataset | Modality | Number of Embeddings | Sensing Type | Total Comments | Source Dataset | Source Model | Size | |
| |:--------:|:--------------:|:-------------------:|:------------:|:--------------:|:--------------:|:--------------:|:--------------:| |
| |Core-S2RGB-DINOv2|Sentinel-2 Level 2A (RGB)|56,147,150|True Colour (RGB)|General-Purpose Global|[Core-S2L2A](https://huggingface.co/datasets/Major-TOM/Core-S2L2A)|[DINOv2](https://huggingface.co/docs/transformers/en/model_doc/dinov2)|223.1 GB| |
|
|
| ## Overview |
| This dataset provides global, dense embeddings of Earth surface imagery extracted using a pre-trained DINOv2 model. It is part of the Major TOM project and is featured in the paper **"EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images"**. |
|
|
| ## Content |
| | Field | Type | Description | |
| |:-----------------:|:--------:|-----------------------------------------------------------------------------| |
| | unique_id | string | hash generated from geometry, time, product_id, and embedding model | |
| | embedding | array | raw embedding array | |
| | grid_cell | string | Major TOM cell | |
| | grid_row_u | int | Major TOM cell row | |
| | grid_col_r | int | Major TOM cell col | |
| | product_id | string | ID of the original product | |
| | timestamp | string | Timestamp of the sample | |
| | centre_lat | float | Centre of the fragment latitude | |
| | centre_lon | float | Centre of the fragment longitude | |
| | geometry | geometry | Polygon footprint (WGS84) of the fragment | |
| | utm_footprint | string | Polygon footprint (image UTM) of the fragment | |
| | utm_crs | string | CRS of the original product | |
| | pixel_bbox | bbox | Boundary box of the fragment (pixels) |
| |
| |
| ## Input Data |
| * Sentinel-2 (Level 2A) RGB reflectance multiplied by 2.5 and clipped between 0 and 1 to resemble images in the training data |
| * All samples from [**MajorTOM Core-S2LA**](https://huggingface.co/datasets/Major-TOM/Core-S2L2A) |
| * Image input size: **224 x 224** pixels, target overlap: 10%, border_shift: True |
|
|
| ## Model |
| The image encoder of the [**DINOv2 model**](https://huggingface.co/docs/transformers/en/model_doc/dinov2) was used to extract embeddings. |
|
|
| ## Example Use |
| Interface scripts are available at |
|
|
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("Major-TOM/Core-S2RGB-DINOv2") |
| ``` |
|
|
| ## Generate Your Own Major TOM Embeddings |
|
|
| The [**embedder**](https://github.com/ESA-PhiLab/Major-TOM/tree/main/src/embedder) subpackage of Major TOM provides tools for generating embeddings like these ones. You can see an example of this in a dedicated notebook at https://github.com/ESA-PhiLab/Major-TOM/blob/main/05-Generate-Major-TOM-Embeddings.ipynb. |
|
|
| [](https://github.com/ESA-PhiLab/Major-TOM/blob/main/05-Generate-Major-TOM-Embeddings.ipynb) |
|
|
| --- |
| ## Major TOM Global Embeddings Project 🏭 |
| This dataset is a result of a collaboration between [**CloudFerro**](https://cloudferro.com/) 🔶 and [**Φ-lab, European Space Agency (ESA)**](https://philab.esa.int/) 🛰️ set up in order to provide open and free vectorised expansions of Major TOM datasets and define a standardised manner for releasing Major TOM embedding expansions. |
|
|
| The embeddings extracted from common AI models make it possible to browse and navigate large datasets like Major TOM with reduced storage and computational demand. |
|
|
| The datasets were computed on the [**GPU-accelerated instances**](https://cloudferro.com/ai/ai-computing-services/)⚡ provided by [**CloudFerro**](https://cloudferro.com/) 🔶 on the [**CREODIAS**](https://creodias.eu/) cloud service platform 💻☁️. |
| Discover more at [**CloudFerro AI services**](https://cloudferro.com/ai/). |
|
|
| ## Authors |
| [**Mikolaj Czerkawski**](https://mikonvergence.github.io) (Φ-lab, European Space Agency), [**Marcin Kluczek**](https://www.linkedin.com/in/marcin-kluczek-03852a1a8/) (CloudFerro), [**Jędrzej S. Bojanowski**](https://www.linkedin.com/in/j%C4%99drzej-s-bojanowski-a5059872/) (CloudFerro) |
|
|
| ## Citation |
|
|
| This dataset is an output from the embedding expansion project outlined in: [https://arxiv.org/abs/2412.05600/](https://arxiv.org/abs/2412.05600/). |
|
|
| ```latex |
| @misc{EmbeddedMajorTOM, |
| title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space}, |
| author={Mikolaj Czerkawski and Marcin Kluczek and Jędrzej S. Bojanowski}, |
| year={2024}, |
| eprint={2412.05600}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2412.05600}, |
| } |
| ``` |
|
|
| For the EarthEmbeddingExplorer tool and cross-modal retrieval workflows, please refer to: |
| [EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images](https://huggingface.co/papers/2603.29441). |
|
|
| Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://philab.esa.int/) in collaboration with [CloudFerro 🔶](https://cloudferro.com/) |
