Talks and Poster Presentations (with Proceedings-Entry):
M. McCutchan, I. Giannopoulos:
"Geospatial Semantics for Spatial Prediction";
Talk: 10th International Conference on Geographic Information Science (GIScience 2018),
Melbourne;
2018-08-28
- 2018-08-31; in: "Proceedings 10th International Conference on Geographic Information Science (GIScience 2018)",
S. Winter, A. Griffin, M. Sester (ed.);
LIPICS,
114
(2018),
ISBN: 978-3-95977-083-5;
45:1
- 45:6.
English abstract:
In this paper the potential of geospatial semantics for spatial predictions is explored. Therefore data from the LinkedGeoData platform is used to predict landcover classes described by the CORINE dataset. Geo-objects obtained from LinkedGeoData are described by an OWL ontology, which is utilized for the purpose of spatial prediction within this paper. This prediction is based on an association analysis which computes the collocations between the landcover classes and the semantically described geo-objects. The paper provides an analysis of the learned association rules and finally concludes with a discussion on the promising potential of geospatial semantics for spatial predictions, as well as potentially fruitful future research within this domain.
German abstract:
In this paper the potential of geospatial semantics for spatial predictions is explored. Therefore data from the LinkedGeoData platform is used to predict landcover classes described by the CORINE dataset. Geo-objects obtained from LinkedGeoData are described by an OWL ontology, which is utilized for the purpose of spatial prediction within this paper. This prediction is based on an association analysis which computes the collocations between the landcover classes and the semantically described geo-objects. The paper provides an analysis of the learned association rules and finally concludes with a discussion on the promising potential of geospatial semantics for spatial predictions, as well as potentially fruitful future research within this domain.
Keywords:
Geospatial semantics, spatial prediction, machine learning, Linked Data
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.4230/LIPIcs.GIScience.2018.45
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_271425.pdf
Created from the Publication Database of the Vienna University of Technology.