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@@ -75,15 +75,31 @@ Discover more at [**CloudFerro AI services**](https://cloudferro.com/ai/).
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  ## Authors
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  [**Marcin Kluczek (CloudFerro)**](https://www.linkedin.com/in/marcin-kluczek-03852a1a8/), [**Mikolaj Czerkawski (Φ-lab, European Space Agency)**](https://mikonvergence.github.io), [**Jędrzej S. Bojanowski (CloudFerro)**](https://www.linkedin.com/in/j%C4%99drzej-s-bojanowski-a5059872/)
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- # Cite
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- ## Cite
 
 
 
 
 
 
 
 
 
 
 
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  ```latex
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- title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space},
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- author={Mikolaj Czerkawski, Marcin Kluczek, Jędrzej S. Bojanowski},
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- year={2024},
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- archivePrefix={arXiv},
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- primaryClass={cs.CV}
 
 
 
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  }
 
 
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  Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://philab.esa.int/) in collaboration with [CloudFerro 🔶](https://cloudferro.com/)
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  ## Authors
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  [**Marcin Kluczek (CloudFerro)**](https://www.linkedin.com/in/marcin-kluczek-03852a1a8/), [**Mikolaj Czerkawski (Φ-lab, European Space Agency)**](https://mikonvergence.github.io), [**Jędrzej S. Bojanowski (CloudFerro)**](https://www.linkedin.com/in/j%C4%99drzej-s-bojanowski-a5059872/)
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+ ## Open Access Manuscript
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+
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+ [![arxiv](https://img.shields.io/badge/Open_Access-arxiv:2402.12095-b31b1b)](https://arxiv.org/abs/2412.05600/)
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+ This project has been outlined in [https://arxiv.org/abs/2412.05600/](https://arxiv.org/abs/2412.05600/).
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+ <details>
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+ <summary>Read Abstract</summary>
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+
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+ > 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.
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+ > </details>
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+
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+
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+ Please cite accordingly as:
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  ```latex
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+ @misc{czerkawski2024globaldenseembeddingsearth,
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+ title={Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space},
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+ author={Mikolaj Czerkawski and Marcin Kluczek and Jędrzej S. Bojanowski},
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+ year={2024},
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+ eprint={2412.05600},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2412.05600},
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  }
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+ ```
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+
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  Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://philab.esa.int/) in collaboration with [CloudFerro 🔶](https://cloudferro.com/)
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