Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year
  • CiteScore value: 2.43 CiteScore
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 73 Scimago H
    index 73
Volume 15, issue 6 | Copyright
Nat. Hazards Earth Syst. Sci., 15, 1087-1101, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Jun 2015

Research article | 01 Jun 2015

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

J. Fernandez Galarreta, N. Kerle, and M. Gerke J. Fernandez Galarreta et al.
  • Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands

Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

Publications Copernicus