Ontology-based classification of building types detected from airborne laser scanning data

Mariana Belgiu*, Ivan Tomljenovic, Thomas J. Lampoltshammer, Thomas Blaschke, Bernhard Höfle

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Abstract: Accurate information on 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).
Original languageEnglish
Pages (from-to)1347-1366
Number of pages20
JournalRemote Sensing
Volume6
Issue number2
DOIs
Publication statusPublished - 1 Feb 2014

Fields of Science and Technology Classification 2012

  • 102 Computer Sciences
  • 507 Human Geography, Regional Geography, Regional Planning
  • 211 Other Technical Sciences
  • 105 Geosciences

Keywords

  • Airborne laser scanning
  • Buildings
  • OBIA
  • Ontology
  • Random forest

Cite this