<div class="csl-bib-body">
<div class="csl-entry">McCutchan, M., & Giannopoulos, I. (2018). Geospatial Semantics for Spatial Prediction (Short Paper). In S. Winter, A. Griffin, & M. Sester (Eds.), <i>10th International Conference on Geographic Information Science (GIScience 2018)</i> (pp. 45:1-45:6). LIPICS. https://doi.org/10.4230/LIPIcs.GIScience.2018.45</div>
</div>
-
dc.identifier.isbn
978-3-95977-083-5
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/43866
-
dc.description.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.
de
dc.description.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.
en
dc.language.iso
en
-
dc.publisher
LIPICS
-
dc.relation.ispartofseries
Leibniz International Proceedings in Informatics (LIPIcs)
-
dc.subject
machine learning
-
dc.subject
Geospatial semantics
-
dc.subject
spatial prediction
-
dc.subject
Linked Data
-
dc.title
Geospatial Semantics for Spatial Prediction (Short Paper)
en
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
10th International Conference on Geographic Information Science (GIScience 2018)
-
dc.relation.isbn
978-3-95977-083-5
-
dc.relation.issn
1868-8969
-
dc.description.startpage
45:1
-
dc.description.endpage
45:6
-
dc.type.category
Full-Paper Contribution
-
dc.publisher.place
114
-
tuw.booktitle
10th International Conference on Geographic Information Science (GIScience 2018)
-
tuw.container.volume
114
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
-
tuw.relation.publisherplace
Dagstuhl
-
tuw.researchTopic.id
X1
-
tuw.researchTopic.name
außerhalb der gesamtuniversitären Forschungsschwerpunkte
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E120-02 - Forschungsbereich Geoinformation
-
tuw.publisher.doi
10.4230/LIPIcs.GIScience.2018.45
-
dc.description.numberOfPages
6
-
tuw.event.name
10th International Conference on Geographic Information Science (GIScience 2018)
