@misc{DelikostidisEngelRetsiosetal.2013,
  author    = {Delikostidis, Ioannis and Engel, Juri and Retsios, Bas and Elzakker, Corn{\´e} P.J.M. van and Kraak, Menno-Jan and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Increasing the usability of pedestrian navigation interfaces by means of landmark visibility analysis},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {599},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41550},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-415500},
  pages     = {523 -- 537},
  year      = {2013},
  abstract  = {Communicating location-specific information to pedestrians is a challenging task which can be aided by user-friendly digital technologies. In this paper, landmark visibility analysis, as a means for developing more usable pedestrian navigation systems, is discussed. Using an algorithmic framework for image-based 3D analysis, this method integrates a 3D city model with identified landmarks and produces raster visibility layers for each one. This output enables an Android phone prototype application to indicate the visibility of landmarks from the user's actual position. Tested in the field, the method achieves sufficient accuracy for the context of use and improves navigation efficiency and effectiveness.},
  language  = {en}
}
@article{DelikostidisEngelRetsiosetal.2013,
  author    = {Delikostidis, Ioannis and Engel, Juri and Retsios, Bas and van Elzakker, Corne P. J. M. and Kraak, Menno-Jan and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Increasing the usability of pedestrian navigation interfaces by means of landmark visibility analysis},
  series = {The journal of navigation},
  volume    = {66},
  journal   = {The journal of navigation},
  number    = {4},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {0373-4633},
  doi       = {10.1017/S0373463313000209},
  pages     = {523 -- 537},
  year      = {2013},
  abstract  = {Communicating location-specific information to pedestrians is a challenging task which can be aided by user-friendly digital technologies. In this paper, landmark visibility analysis, as a means for developing more usable pedestrian navigation systems, is discussed. Using an algorithmic framework for image-based 3D analysis, this method integrates a 3D city model with identified landmarks and produces raster visibility layers for each one. This output enables an Android phone prototype application to indicate the visibility of landmarks from the user's actual position. Tested in the field, the method achieves sufficient accuracy for the context of use and improves navigation efficiency and effectiveness.},
  language  = {en}
}
@article{RichterKyprianidisDoellner2013,
  author    = {Richter, Rico and Kyprianidis, Jan Eric and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Out-of-core GPU-based change detection in massive 3D point clouds},
  series = {Transactions in GIS},
  volume    = {17},
  journal   = {Transactions in GIS},
  number    = {5},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1361-1682},
  doi       = {10.1111/j.1467-9671.2012.01362.x},
  pages     = {724 -- 741},
  year      = {2013},
  abstract  = {If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out-of-core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point-based rendering technique adapted for attributed 3D point clouds, to enable effective out-of-core real-time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.},
  language  = {en}
}