title: Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
    Space
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.
paper_authors._id: 6757e3ad6b8a608297ec0356
paper_authors.name: Mikolaj Czerkawski
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-10T10:25:13.544Z
paper_authors._id: 6304c06eeb6d777a838eab63
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1678741407493-6304c06eeb6d777a838eab63.png
paper_authors.fullname: Mikolaj Czerkawski
paper_authors.type: user
paper_authors.user: mikonvergence
paper_authors._id: 6757e3ad6b8a608297ec0357
paper_authors.name: Marcin Kluczek
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-12-10T06:46:11.399Z
paper_authors._id: 66aa04a381dd5b0fe363b4da
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66aa04a381dd5b0fe363b4da/2bJIilWC1Oqm323qSrMO2.png
paper_authors.fullname: Marcin Kluczek
paper_authors.type: user
paper_authors.user: mkluczek
paper_authors._id: 6757e3ad6b8a608297ec0358
paper_authors.name: Jędrzej S. Bojanowski
paper_title: Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
    Space
paper_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.
paper_ai_summary: Feature extraction from pretrained deep neural networks is extended to provide semantic abstractions for Earth observation data, with four global geospatial embedding datasets released openly.
paper_ai_keywords: pretrained deep neural networks geospatial data Major TOM open and free AI-ready datasets geospatial visual embeddings