Extracting Urban Land Use from Linked Open Geospatial Data
AbstractThe 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
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.
Calegari GR, Carlino E, Peroni D, Celino I. Extracting Urban Land Use from Linked Open Geospatial Data. ISPRS International Journal of Geo-Information. 2015; 4(4):2109-2130.Chicago/Turabian Style
Calegari, Gloria R.; Carlino, Emanuela; Peroni, Diego; Celino, Irene. 2015. "Extracting Urban Land Use from Linked Open Geospatial Data." ISPRS Int. J. Geo-Inf. 4, no. 4: 2109-2130.