GeoVectors-Antarctica-location (v0.1)

Description The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap...

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Bibliographic Details
Main Authors: Tempelmeier, Nicolas, Gottschalk, Simon, Demidova, Elena
Format: Dataset
Language:unknown
Published: 2020
Subjects:
Online Access:https://zenodo.org/record/4322563
https://doi.org/10.5281/zenodo.4322563
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spelling ftzenodo:oai:zenodo.org:4322563 2023-05-15T13:38:14+02:00 GeoVectors-Antarctica-location (v0.1) Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena 2020-12-15 https://zenodo.org/record/4322563 https://doi.org/10.5281/zenodo.4322563 unknown doi:10.5281/zenodo.4322562 https://zenodo.org/record/4322563 https://doi.org/10.5281/zenodo.4322563 oai:zenodo.org:4322563 info:eu-repo/semantics/openAccess https://opendatacommons.org/licenses/odbl/1-0/ info:eu-repo/semantics/other dataset 2020 ftzenodo https://doi.org/10.5281/zenodo.432256310.5281/zenodo.4322562 2023-03-10T16:19:27Z Description The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. Contents This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Antarctica" including the following countries: Antarctica File format The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) Further information: For further information, please visit http://geovectors.l3s.uni-hannover.de Funding: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477). Dataset Antarc* Antarctica Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
description Description The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. Contents This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains location-embeddings for the region "Antarctica" including the following countries: Antarctica File format The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) Further information: For further information, please visit http://geovectors.l3s.uni-hannover.de Funding: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
format Dataset
author Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
spellingShingle Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
GeoVectors-Antarctica-location (v0.1)
author_facet Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
author_sort Tempelmeier, Nicolas
title GeoVectors-Antarctica-location (v0.1)
title_short GeoVectors-Antarctica-location (v0.1)
title_full GeoVectors-Antarctica-location (v0.1)
title_fullStr GeoVectors-Antarctica-location (v0.1)
title_full_unstemmed GeoVectors-Antarctica-location (v0.1)
title_sort geovectors-antarctica-location (v0.1)
publishDate 2020
url https://zenodo.org/record/4322563
https://doi.org/10.5281/zenodo.4322563
genre Antarc*
Antarctica
genre_facet Antarc*
Antarctica
op_relation doi:10.5281/zenodo.4322562
https://zenodo.org/record/4322563
https://doi.org/10.5281/zenodo.4322563
oai:zenodo.org:4322563
op_rights info:eu-repo/semantics/openAccess
https://opendatacommons.org/licenses/odbl/1-0/
op_doi https://doi.org/10.5281/zenodo.432256310.5281/zenodo.4322562
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