Volume I-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 293-298, 2012
https://doi.org/10.5194/isprsannals-I-3-293-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 293-298, 2012
https://doi.org/10.5194/isprsannals-I-3-293-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 Jul 2012

20 Jul 2012

THE ISPRS BENCHMARK ON URBAN OBJECT CLASSIFICATION AND 3D BUILDING RECONSTRUCTION

F. Rottensteiner1, G. Sohn2, J. Jung2, M. Gerke3, C. Baillard4, S. Benitez4, and U. Breitkopf1 F. Rottensteiner et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2GeoICT Lab, Earth and Space Science and Engineering Department, York University, Toronto, Canada
  • 3University of Twente, Faculty ITC, EOS department, Enschede, The Netherlands
  • 4SIRADEL, Rennes, France

Keywords: Automatic object extraction, 3D building reconstruction, aerial imagery, laser scanning, evaluation, test

Abstract. For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.