Published on 15 December 2020 |

Version 1.0

GeoVectors-Australia-Oceania-location (v0.1)

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Tempelmeier, Nicolas;Gottschalk, Simon;Demidova, Elena

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 "Australia-Oceania" including the following countries: American-Oceania Australia Cook-Islands Fiji Ile-De-Clipperton Kiribati Marshall-Islands Micronesia Nauru New-Caledonia New-Zealand Niue Palau Papua-New-Guinea Pitcairn-Islands Polynesie-Francaise Samoa Solomon-Islands Tokelau Tonga Tuvalu Vanuatu Wallis-Et-Futuna 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).

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.3

FAIR Score

13%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

Assigned Domain

Subfield

Geography, Planning and Development

Field

Social Sciences

Domain

Social Sciences

Confidence Score

59%

Source

Open Alex

Normalization Factors

FT

15.38

CTw

1.00

MTw

1.00