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1

School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg 5412, Austria

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Department of Geoinformatics Z GIS, University of Salzburg, Schillerstrasse 30, Salzburg 5020, Austria

3

IT Innovation Centre, University of Southampton, Gamma House, Enterprise Road, Southampton SO16 7NS, UK





*

Author to whom correspondence should be addressed.



Academic Editors: Ioannis Gitas and Prasad S. Thenkabail

Abstract The increasing availability of very high-resolution remote sensing imagery i.e., from satellites, airborne laser scanning, or aerial photography represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, semi- automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach. View Full-Text

Keywords: ontology; graph database; Neo4j; remote sensing; classification ontology; graph database; Neo4j; remote sensing; classification





Autor: Thomas J. Lampoltshammer 1,2,* and Stefanie Wiegand 3

Fuente: http://mdpi.com/



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