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Remote Sens. 2014, 6(2), 1347-1366; doi:10.3390/rs6021347

Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

1
Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
2
School of Information Technology and Systems Management, Salzburg University of Applied Sciences, 5412 Puch-Salzburg, Austria
3
Institute of Geography, Chair of GIScience, University of Heidelberg, 69120 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
Received: 19 November 2013 / Revised: 15 January 2014 / Accepted: 5 February 2014 / Published: 12 February 2014
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Abstract

Accurate information on urban building types plays a crucial role for urban development, planning, and management. In this paper, we apply Object-Based Image Analysis (OBIA) methods to extract buildings from Airborne Laser Scanner (ALS) data and investigate the possibility of classifying detected buildings into “Residential/Small Buildings”, “Apartment Buildings”, and “Industrial and Factory Building” classes by means of domain ontology and machine learning techniques. The buildings objects are classified using exclusively the information computed from the ALS data. To select the relevant features for predicting the classes of interest, the Random Forest classifier has been applied. The ontology-based classification yielded convincing results for the “Residential/Small Buildings” class (F-Measure 97.7%), whereas the “Apartment Buildings” and “Industrial and Factory Buildings” classes achieved less accurate results (F-Measure 60% and 51%, respectively).
Keywords: buildings; OBIA; ontology; Random Forest; Airborne Laser Scanning buildings; OBIA; ontology; Random Forest; Airborne Laser Scanning
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Belgiu, M.; Tomljenovic, I.; Lampoltshammer, T.J.; Blaschke, T.; Höfle, B. Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data. Remote Sens. 2014, 6, 1347-1366.

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