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MatchingLand, geospatial data testbed for the assessment of matching methods

This article presents datasets prepared with the aim of helping the evaluation of geospatial matching methods for vector data. These datasets were built up from mapping data produced by official Spanish mapping agencies. The testbed supplied encompasses the three geometry types: point, line and area...

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Autores principales: Xavier, Emerson M. A., Ariza-López, Francisco J., Ureña-Cámara, Manuel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716014/
https://www.ncbi.nlm.nih.gov/pubmed/29206220
http://dx.doi.org/10.1038/sdata.2017.180
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author Xavier, Emerson M. A.
Ariza-López, Francisco J.
Ureña-Cámara, Manuel A.
author_facet Xavier, Emerson M. A.
Ariza-López, Francisco J.
Ureña-Cámara, Manuel A.
author_sort Xavier, Emerson M. A.
collection PubMed
description This article presents datasets prepared with the aim of helping the evaluation of geospatial matching methods for vector data. These datasets were built up from mapping data produced by official Spanish mapping agencies. The testbed supplied encompasses the three geometry types: point, line and area. Initial datasets were submitted to geometric transformations in order to generate synthetic datasets. These transformations represent factors that might influence the performance of geospatial matching methods, like the morphology of linear or areal features, systematic transformations, and random disturbance over initial data. We call our 11 GiB benchmark data ‘MatchingLand’ and we hope it can be useful for the geographic information science research community.
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spelling pubmed-57160142017-12-13 MatchingLand, geospatial data testbed for the assessment of matching methods Xavier, Emerson M. A. Ariza-López, Francisco J. Ureña-Cámara, Manuel A. Sci Data Data Descriptor This article presents datasets prepared with the aim of helping the evaluation of geospatial matching methods for vector data. These datasets were built up from mapping data produced by official Spanish mapping agencies. The testbed supplied encompasses the three geometry types: point, line and area. Initial datasets were submitted to geometric transformations in order to generate synthetic datasets. These transformations represent factors that might influence the performance of geospatial matching methods, like the morphology of linear or areal features, systematic transformations, and random disturbance over initial data. We call our 11 GiB benchmark data ‘MatchingLand’ and we hope it can be useful for the geographic information science research community. Nature Publishing Group 2017-12-05 /pmc/articles/PMC5716014/ /pubmed/29206220 http://dx.doi.org/10.1038/sdata.2017.180 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Xavier, Emerson M. A.
Ariza-López, Francisco J.
Ureña-Cámara, Manuel A.
MatchingLand, geospatial data testbed for the assessment of matching methods
title MatchingLand, geospatial data testbed for the assessment of matching methods
title_full MatchingLand, geospatial data testbed for the assessment of matching methods
title_fullStr MatchingLand, geospatial data testbed for the assessment of matching methods
title_full_unstemmed MatchingLand, geospatial data testbed for the assessment of matching methods
title_short MatchingLand, geospatial data testbed for the assessment of matching methods
title_sort matchingland, geospatial data testbed for the assessment of matching methods
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716014/
https://www.ncbi.nlm.nih.gov/pubmed/29206220
http://dx.doi.org/10.1038/sdata.2017.180
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