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      MatchingLand, geospatial data testbed for the assessment of matching methods

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          Abstract

          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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          Most cited references28

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          ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE

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            Smart city policies: A spatial approach

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              Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb

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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                05 December 2017
                2017
                : 4
                : 170180
                Affiliations
                [1 ]Brazilian Army Geographic Service, Brasilia 70630-901 , Brazil
                [2 ]Universidad de Jaén, Jaén 23071 , Spain
                Author notes
                [a ] E.M.A.X. (email: emerson.xavier@ 123456eb.mil.br ).
                []

                All authors collaborated in designing the study and writing the paper. E.X. performed the software development.

                Author information
                http://orcid.org/0000-0003-2737-6633
                http://orcid.org/0000-0002-6373-4410
                Article
                sdata2017180
                10.1038/sdata.2017.180
                5716014
                29206220
                c841b892-f342-4dd1-b71f-d1c729ef5244
                Copyright © 2017, The Author(s)

                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.

                History
                : 16 February 2017
                : 19 October 2017
                Categories
                Data Descriptor

                geography,information technology
                geography, information technology

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