Add task category and reference paper for EarthEmbeddingExplorer

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
@@ -10,16 +14,24 @@ 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/_pmXpKIwlYdPWG7j-nR05.png)
 # Core-S1RTC-SSL4EO 📡⚡🛰️
+
+This dataset provides global embeddings for Sentinel-1 RTC imagery, enabling efficient cross-modal retrieval and analysis. It is a core component of the [EarthEmbeddingExplorer](https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer) application.
+
+**Associated Papers:**
+- **EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images** ([arXiv:2603.29441](https://huggingface.co/papers/2603.29441))
+- **Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space** ([arXiv:2412.05600](https://huggingface.co/papers/2412.05600))
+
+**Links:**
+- **Project Page:** [EarthEmbeddingExplorer](https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer)
+- **GitHub Repository:** [ESA-PhiLab/Major-TOM](https://github.com/ESA-PhiLab/Major-TOM)
+
 | Dataset | Modality | Number of Embeddings  | Sensing Type | Total Comments | Source Dataset | Source Model | Size |
 |:--------:|:--------------:|:-------------------:|:------------:|:--------------:|:--------------:|:--------------:|:--------------:|
 |Core-S1RTC-SSL4EO|Sentinel-1 RTC|36,748,875|SAR|General-Purpose Global|[Core-S1RTC](https://huggingface.co/datasets/Major-TOM/Core-S1RTC)|[SSL4EO-ResNet50-MOCO](https://github.com/zhu-xlab/SSL4EO-S12)|332.5 GB|
@@ -36,7 +48,7 @@ configs:
 | 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                                            |
+| 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                                                 |
@@ -78,7 +90,7 @@ 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
+## Open Access Manuscripts
 
 This dataset is an output from the embedding expansion project outlined in: [https://arxiv.org/abs/2412.05600/](https://arxiv.org/abs/2412.05600/).
 
@@ -89,7 +101,7 @@ This dataset is an output from the embedding expansion project outlined in: [htt
 <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>
+ </details>
 
 
 If this dataset was useful for you work, it can be cited as:
@@ -103,6 +115,13 @@ If this dataset was useful for you work, it can be cited as:
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2412.05600}, 
 }
+
+@article{Zheng2026EarthEmbeddingExplorer,
+  title={EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images},
+  author={Yijie Zheng and Weijie Wu and Bingyue Wu and Long Zhao and Guoqing Li and Mikolaj Czerkawski and Konstantin Klemmer},
+  year={2026},
+  journal={arXiv preprint arXiv:2603.29441}
+}
 ```