Published June 15, 2021 | Version 1.0
Dataset Open

GeoVectors-Europe-West-location (v1.0)

  • 1. L3S Research Center, Leibniz University Hannover, Germany
  • 2. Data Science & Intelligent Systems Group (DSIS), University of Bonn, Germany

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 dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension.

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 "Europe-west" including the following countries:

  • Andorra
  • Austria
  • Azores
  • Belgium
  • Denmark
  • Faroe-Islands
  • Guernsey-Jersey
  • Ireland-and-Northern-Ireland
  • Isle-Of-Man
  • Liechtenstein
  • Luxembourg
  • Malta
  • Monaco
  • Netherlands
  • Norway
  • Portugal
  • Spain
  • Switzerland

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).

Files

Files (41.9 GB)

Name Size Download all
md5:9e9a831d204096c63e4939e439f01944
10.5 MB Download
md5:4a57f38a683df4d80e5ceafcf9238f6e
4.5 GB Download
md5:a4f65b2408faa16b94e9ef73d2707b21
65.8 MB Download
md5:452621c0e0861ae036386bd40fa99e61
3.6 GB Download
md5:c72a1cf99c23af030a1bbb3b3c6d73d0
3.8 GB Download
md5:87309ab2bd08646e4f1af5916000a291
39.7 MB Download
md5:7a5308d853b13db0b104d27675144ec5
24.3 MB Download
md5:26381b842a88278272c49de0c8c04188
298 Bytes Download
md5:818f08d1b19c5427d48d1c9aac087bb4
1.6 GB Download
md5:2543bdc7c6c21780950c5f2de8bbf5fc
18.9 MB Download
md5:f14b1918da2a4762dd55447b2b06f2dd
20.2 MB Download
md5:bfd88795ca0e51426e1646d47c86abf1
220.2 MB Download
md5:86d101d2a0e1dda4a2ffbb467ba60406
38.2 MB Download
md5:fb79e68472773bfbbb756c43bdbd8107
3.1 MB Download
md5:ca5955fff005cc2bb679ee860f0e9331
11.8 GB Download
md5:76e3c3c8521a32a56d87641bdad58abd
5.3 GB Download
md5:3ba55dca0f75f01247bb9c0db6edf1e0
1.5 GB Download
md5:8ef738c4da274ca0d44050d003d7f7d2
6.5 GB Download
md5:69f74ae9a29c294d05e8f0ef6cb827db
2.8 GB Download