2026-07-25T02:34:10Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9373 2024-03-06T10:44:10Z ddc:004 open_access

Geospatial Semantics for Spatial Prediction (Short Paper) Mc Cutchan, Marvin Giannopoulos, Ioannis Geospatial semantics spatial prediction machine learning Linked Data 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Marvin Mc Cutchan and Ioannis Giannopoulos 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.45 urn:nbn:de:0030-drops-93731 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.45 eng https://creativecommons.org/licenses/by/3.0/legalcode