Extracting Urban Land Use from Linked Open Geospatial Data
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 in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2220-9964/4/4/2109 |
| _version_ | 1830476447334006784 |
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| author | Gloria Re Calegari Emanuela Carlino Diego Peroni Irene Celino |
| author_facet | Gloria Re Calegari Emanuela Carlino Diego Peroni Irene Celino |
| author_sort | Gloria Re Calegari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T15:53:19Z |
| format | Article |
| id | doaj.art-b5b8d17b743941b893928f846862584a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T15:53:19Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-b5b8d17b743941b893928f846862584a2022-12-21T18:58:10ZengMDPI AGISPRS International Journal of Geo-Information2220-99642015-10-01442109213010.3390/ijgi4042109ijgi4042109Extracting Urban Land Use from Linked Open Geospatial DataGloria Re Calegari0Emanuela Carlino1Diego Peroni2Irene Celino3ICT Center of Excellence For Research, Innovation, Education and industrial Labs partnerships (CEFRIEL), Politecnico di Milano, via Fucini 2, 20133 Milano, ItalyICT Center of Excellence For Research, Innovation, Education and industrial Labs partnerships (CEFRIEL), Politecnico di Milano, via Fucini 2, 20133 Milano, ItalyICT Center of Excellence For Research, Innovation, Education and industrial Labs partnerships (CEFRIEL), Politecnico di Milano, via Fucini 2, 20133 Milano, ItalyICT Center of Excellence For Research, Innovation, Education and industrial Labs partnerships (CEFRIEL), Politecnico di Milano, via Fucini 2, 20133 Milano, ItalyThe 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.http://www.mdpi.com/2220-9964/4/4/2109urban land uselinked open geo-spatial datapoints of interestsmart cities |
| spellingShingle | Gloria Re Calegari Emanuela Carlino Diego Peroni Irene Celino Extracting Urban Land Use from Linked Open Geospatial Data ISPRS International Journal of Geo-Information urban land use linked open geo-spatial data points of interest smart cities |
| title | Extracting Urban Land Use from Linked Open Geospatial Data |
| title_full | Extracting Urban Land Use from Linked Open Geospatial Data |
| title_fullStr | Extracting Urban Land Use from Linked Open Geospatial Data |
| title_full_unstemmed | Extracting Urban Land Use from Linked Open Geospatial Data |
| title_short | Extracting Urban Land Use from Linked Open Geospatial Data |
| title_sort | extracting urban land use from linked open geospatial data |
| topic | urban land use linked open geo-spatial data points of interest smart cities |
| url | http://www.mdpi.com/2220-9964/4/4/2109 |
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