Add task category and link to EarthEmbeddingExplorer paper

README.md

@@ -1,5 +1,9 @@
 ---
 license: cc-by-sa-4.0
+size_categories:
+- 10M<n<100M
+task_categories:
+- image-feature-extraction
 tags:
 - embeddings
 - earth-observation
@@ -8,21 +12,23 @@ tags:
 - satellite
 - geospatial
 - satellite-imagery
-size_categories:
-- 10M<n<100M
 configs:
 - config_name: default
   data_files: embeddings/*.parquet
 ---
 
-
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/Z46c9v4OhCO_e7yc-d4KS.png)
 
 # 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-SigLIP|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|
+|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                                                                 |
@@ -48,7 +54,7 @@ configs:
 * 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/dinov) was used to extract embeddings.
+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
@@ -76,20 +82,10 @@ 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)
 
-## Open Access Manuscript
+## 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/).
 
-[![arXiv](https://img.shields.io/badge/arXiv-10.48550/arXiv.2412.05600-B31B1B.svg)](https://doi.org/10.48550/arXiv.2412.05600)
-
-<details>
-<summary>Read Abstract</summary>
-  
-  > With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data. However, the way this is done for imagery archives containing geospatial data has not yet been defined. In this work, an extension is proposed to an existing community project, Major TOM, focused on the provision and standardization of open and free AI-ready datasets for Earth observation. Furthermore, four global and dense embedding datasets are released openly and for free along with the publication of this manuscript, resulting in the most comprehensive global open dataset of geospatial visual embeddings in terms of covered Earth's surface.
-> </details>
-
-
-If this dataset was useful for you work, it can be cited as:
 ```latex
 @misc{EmbeddedMajorTOM,
       title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space}, 
@@ -102,6 +98,7 @@ If this dataset was useful for you work, it can be cited as:
 }
 ```
 
+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/)
-
+Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://philab.esa.int/) in collaboration with [CloudFerro 🔶](https://cloudferro.com/)