Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
  Space
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.
6757e3ad6b8a608297ec0356
Mikolaj Czerkawski
claimed_verified
2024-12-10T10:25:13.544Z
6304c06eeb6d777a838eab63
https://cdn-avatars.huggingface.co/v1/production/uploads/1678741407493-6304c06eeb6d777a838eab63.png
Mikolaj Czerkawski
user
mikonvergence
6757e3ad6b8a608297ec0357
Marcin Kluczek
extracted_pending
2024-12-10T06:46:11.399Z
66aa04a381dd5b0fe363b4da
https://cdn-avatars.huggingface.co/v1/production/uploads/66aa04a381dd5b0fe363b4da/2bJIilWC1Oqm323qSrMO2.png
Marcin Kluczek
user
mkluczek
6757e3ad6b8a608297ec0358
Jędrzej S. Bojanowski
Global and Dense Embeddings of Earth: Major TOM Floating in the Latent
  Space
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.
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.
pretrained deep neural networks geospatial data Major TOM open and free AI-ready datasets geospatial visual embeddings