Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
  Space
Authors: name: Mikolaj Czerkawski, name: Marcin Kluczek, name: Jędrzej S. Bojanowski

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.