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isprs-archives-XLII-4-209-2018.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/85210 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/85210 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/85210 85216 2 239274 document 85216 small.jpg image/png c3bbce0430d75b6a92d1c88dd8a3b8a6 MD5 1663 2018-09-21 08:16:38 https://pure.iiasa.ac.at/id/eprint/15473/6/small.jpg 15473 6 6 image/png other Thumbnails conversion from text to thumbnail_small en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/85210 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/85210 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/85210 archive 353 disk0/00/01/54/73 2018-09-21 08:07:40 2021-08-27 17:30:45 2018-09-21 08:07:40 article show Fonte C. C. Minghini M. Antoniou V. Patriarca J. See L. 8571 0000-0002-2665-7065 pub prog_esm Volunteered Geographic Information, OpenStreetMap, Facebook, Foursquare, Building Function, Automated Classification This paper examines the feasibility of using data from OpenStreetMap (OSM), Facebook and Foursquare as a source of information on the function of buildings. Such information is rarely openly available and if available, would vary between cities by nomenclature, making comparisons between places difficult. Volunteered Geographic Information (VGI) including data from social media represents new potential sources of building function data that have not yet been exploited for this purpose. Using a part of the city of Milan as the study area, building data from OSM and points of interest (POIs) from OSM, Facebook and Foursquare were extracted to derive the building function. This resulted in the classification of building function for more than 80% of the buildings and demonstrated that both Facebook and Foursquare can complement the building function derived from OSM, helping to fill in missing gaps. This preliminary study has demonstrated the potential of this approach for deriving building function information from open data in a simple way yet still requires independent validation with alternative sources as well as extension to other areas that have different amounts of OSM and social media coverage. 2018-09 published ISPRS 10.5194/isprs-archives-XLII-4-209-2018 276 public ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 209-215 TRUE 2194-9034 FALSE no info:eu-repo/semantics/article