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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05600 |
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| _version_ | 1866909419271356416 |
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| author | Czerkawski, Mikolaj Kluczek, Marcin Bojanowski, Jędrzej S. |
| author_facet | Czerkawski, Mikolaj Kluczek, Marcin Bojanowski, Jędrzej S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space Czerkawski, Mikolaj Kluczek, Marcin Bojanowski, Jędrzej S. Computer Vision and Pattern Recognition Databases 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. |
| title | Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space |
| topic | Computer Vision and Pattern Recognition Databases |
| url | https://arxiv.org/abs/2412.05600 |