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ISPRS Int. J. Geo-Inf. 2015, 4(4), 2109-2130; doi:10.3390/ijgi4042109

Extracting Urban Land Use from Linked Open Geospatial Data

ICT Center of Excellence For Research, Innovation, Education and industrial Labs partnerships (CEFRIEL), Politecnico di Milano, via Fucini 2, 20133 Milano, Italy
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Academic Editors: Jochen Schiewe and Wolfgang Kainz
Received: 30 June 2015 / Revised: 29 September 2015 / Accepted: 7 October 2015 / Published: 20 October 2015
(This article belongs to the Special Issue Geo-Information Fostering Innovative Solutions for Smart Cities)
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Abstract

The ever-increasing availability of linked open geospatial data provides an unprecedented source of geo-information to describe urban environments. This wealth of data should be turned into actionable knowledge: for example, open data could be used as a proxy or substitute for closed or expensive information. The successful employment of linked open geospatial data can pave the way for innovative solutions to smart city problems. In this paper, we illustrate a set of experiments that, starting from linked open geospatial data, execute a knowledge discovery process to predict urban semantics. More specifically, we leverage geo-information about points of interests as input in a classification model of land use at a moderate spatial resolution (250 meters) over wide urban areas in Europe. We replicate our experiments in different European cities—Milano, München, Barcelona and Brussels—to ensure the repeatability and generality of our approach, and we explain the experimental conditions, as well as the employed datasets to guarantee reproducibility. We extensively report on quantitative and qualitative evaluation results, to judge the validity, as well as the limitations of our proposed approach. View Full-Text
Keywords: urban land use; linked open geo-spatial data; points of interest; smart cities urban land use; linked open geo-spatial data; points of interest; smart cities
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Calegari, G.R.; Carlino, E.; Peroni, D.; Celino, I. Extracting Urban Land Use from Linked Open Geospatial Data. ISPRS Int. J. Geo-Inf. 2015, 4, 2109-2130.

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