GeoVectors-North-America-tags (v1.0)
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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ftzenodo:oai:zenodo.org:4321449 2024-09-15T18:09:57+00:00 GeoVectors-North-America-tags (v1.0) Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena 2020-12-14 https://doi.org/10.5281/zenodo.4321449 unknown Zenodo https://doi.org/10.5281/zenodo.4321448 https://doi.org/10.5281/zenodo.4321449 oai:zenodo.org:4321449 info:eu-repo/semantics/openAccess ODC Open Database License v1.0 http://www.opendatacommons.org/licenses/odbl/1.0/ info:eu-repo/semantics/other 2020 ftzenodo https://doi.org/10.5281/zenodo.432144910.5281/zenodo.4321448 2024-07-25T13:21:40Z 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 tag-embeddings for the region "North-America" including the following countries: Canada Greenland Mexico 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). Other/Unknown Material Greenland Zenodo |
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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 tag-embeddings for the region "North-America" including the following countries: Canada Greenland Mexico 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 |
Other/Unknown Material |
author |
Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena |
spellingShingle |
Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena GeoVectors-North-America-tags (v1.0) |
author_facet |
Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena |
author_sort |
Tempelmeier, Nicolas |
title |
GeoVectors-North-America-tags (v1.0) |
title_short |
GeoVectors-North-America-tags (v1.0) |
title_full |
GeoVectors-North-America-tags (v1.0) |
title_fullStr |
GeoVectors-North-America-tags (v1.0) |
title_full_unstemmed |
GeoVectors-North-America-tags (v1.0) |
title_sort |
geovectors-north-america-tags (v1.0) |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://doi.org/10.5281/zenodo.4321449 |
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Greenland |
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Greenland |
op_relation |
https://doi.org/10.5281/zenodo.4321448 https://doi.org/10.5281/zenodo.4321449 oai:zenodo.org:4321449 |
op_rights |
info:eu-repo/semantics/openAccess ODC Open Database License v1.0 http://www.opendatacommons.org/licenses/odbl/1.0/ |
op_doi |
https://doi.org/10.5281/zenodo.432144910.5281/zenodo.4321448 |
_version_ |
1810447553597014016 |