Adaptive Visualisation of Landmarks using an MRDB

  • Birgit Elias
  • Mark Hampe
  • Monika Sester


Mobile navigation is one of the most popular applications for small electronic devices like PDA (personal digital assistants). In the last years the main focus of routing applications was on the use in car navigation systems. But with the increasing market and availability of small devices, a new user group comes to the fore: the pedestrian user. Because of the different needs and (technical) limitations of both groups, new concepts and implementations to improve the wayfinding process with routing instructions and their (visual) communication have to be developed. In our paper, we propose the generation of routing information targeted at pedestrians. We first describe the possibilities to extract the potential landmarks from existing datasets. For the visualisation of these landmarks in a map we propose to emphasize them appropriately in order to help the user in orientation and navigation. To this end we introduce maps containing more than one level of detail (LoD’s). A multiple resolution database (MRDB) serves as a basis for these kinds of visualisation.


Background Object Landmark Detection Mobile Navigation Landmark Object Navigation Instruction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Badard, T.(1999): On the automatic retrieval of updates in geographic databases based on geographic data matching tools, Proceedings of the 19th International Cartographic Conference of the ICA, Ottawa, Canada, 1999.Google Scholar
  2. Brenner, C., and Elias, B.(2003): Extracting Landmarks for Car Navigation Systems Using Existing GIS Databases and Laser Scanning, PIA 2003, ISPRSArchives, Vol. XXXIV, Part 3/W8, 17.-19.09.03, München, 2003.Google Scholar
  3. Burnett, G., Smith, D., and May, A. (2002): Supporting the Navigation Task: Characteristics of ‘Good’ Landmarks. In Contemporary Ergonomics 2002, M. Hanson, Ed. London: Taylor & Francis, 2002.Google Scholar
  4. Devogele, T., Trevisan, J., and Raynal, L. (1996): Building a Multiscale Database with Scale-Transition Relationships. Proceedings of the 7th Int. Symposium on Spatial Data Handling, Advances in GIS Research II, pp. 6.19–6.33, Delft, 1996.Google Scholar
  5. Elias, B.(2003): Extracting Landmarks with Data Mining Methods. In Spatial Information Theory: Foundations of Geographic Information Science, Vol. 2825, Lecture Notes in Computer Science, W. Kuhn, M.F. Worboys, and S. Timpf, Eds. Berlin: Springer, 2003, pp. 398–412Google Scholar
  6. Elias, B., and M. Hampe (2003): Kontextbezogene Kartengenerierung für Routing-Anwendungen, Technical Paper, Workshop Design kartenbasierter mobiler Dienste, Mensch und Computer 2003, Stuttgart, 09.09.2003. An electronic version available at Scholar
  7. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds) (1996): Advances in Knowledge Discovery and Data Mining, AAAI Press / The MIT Press, Menlo Park, Califomien, 1996.Google Scholar
  8. Golledge, R. (1996): Human Wayfinding and Cognitive Maps, In Wayfinding Behavior, R. Golledge, Ed. Baltimore: John Hopkins University Press, 1999, pp. 5–46Google Scholar
  9. Hampe, M., Anders, K.-H., and Sester, M. (2003): MRDB Applications for Data Revision and Real-Time Generalisation, Proceedings of 21st International Cartographic Conference, 10.–16. August 2003, Durban/South Africa, 2003.Google Scholar
  10. Harrie, L., Sarjakoski, L. T., and L. Lehto (2002): A variable-scale map for small-display cartography. Proceedings of the Joint International Symposium on "GeoSpatial Theory, Processing and Applications" (ISPRS/Commission IV, SDH2002), Ottawa, Canada, July 8–12, 2002, 6 p, CD-ROM, 2002.Google Scholar
  11. Lovelace, K., Hegarty, M., and Montello, D.(1999): Elements of Good Route Directions in Familiar and Unfamiliar Environments, In Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science, International Conference COSIT ‘99, Proceedings, C. Freksa and D. Mark, Eds. Springer Verlag, Germany, pp. 65–82Google Scholar
  12. Michon, P., and Denis, M. (2001): When and Why Are Visual Landmarks Used in Giving Directions?.-In Spatial Information Theory, International Conference COSIT 2001, Proceedings, D. Montello, Ed. Springer Verlag, 2001, pp. 292–305Google Scholar
  13. Nivala, A.-M., and L. T. Sarjakoski (2003): An Approach to Intelligent Maps: Context Awareness. Proceedings of the workshop W1 "HCI in Mobile Guides 2003". In conjunction with: Fifth International Symposium on Human Computer Interaction with Mobile Devices and Services, Mobile HCI 03, September 8–11, 2003, Udine, Italy, Schmidt-Belz, B. and K. Cheverst, Eds., 2003, pp. 45–50Google Scholar
  14. Quinlan, J.R. (1986): Induction of Decision Trees, Machine Learning, 1, pp. 81–106, 1986.Google Scholar
  15. Reichenbacher, T. (2004): Mobile Cartography — Adaptive Visualisation of Geographic Information on Mobile Devices, Dissertation, Department of Cartography, Technische Universitüt München, München: Verlag Dr. Hut, 2004Google Scholar
  16. Sester, M. (2002): Application Dependent Generalization — The Case of Pedestrian Navigation, IAPRS Vol. 34, Part 4 “Geospatial Theory, Processing and Applications”, Ottawa, Canada, 2002.Google Scholar
  17. Sester, M. (2000): Generalization based on Least Squares Adjustment, International Archives of Photogrammetry and Remote Sensing, Vol. 33, ISPRS, Amsterdam, 2000.Google Scholar
  18. Sester, M., Anders, K.-H., and Walter, V. (1998): Linking Objects of Different Spatial Data Sets by Integration and Aggregation, Geoinformatica 2(4), 335–358, 1998.CrossRefGoogle Scholar
  19. Sorrows, M., and Hirtle, S. (1999): The Nature of Landmarks for Real and Electronic Spaces, In Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science, C. Freksa and D. Mark, Eds. Springer Verlag, pp. 37–50Google Scholar
  20. Weibel, R., and Dutton, G. (1999): Generalising spatial data and dealing with multiple representations. In Geographic Information Systems — Principles and Technical Issues, volume 1, P.A. Longley, M.F. Goodchild, D.J., Maguire and D.W. Rhind, Eds. John Wiley & Sons, 2nd ed., 1999, pp 125–155Google Scholar
  21. Winter, S. (2002): Ontologisches Modellieren von Routen für mobile Navigationsdienste. Telekartographie und Location Based Services, Geowissenschaftliche Mitteilungen, Nr. 58, Schriftenreihe der Studienrichtung Vermessungswesen und Geoinformation, TU Wien, 2002.Google Scholar
  22. Witten, I. H., and Eibe, F. (1999): Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Birgit Elias
    • 1
  • Mark Hampe
    • 1
  • Monika Sester
    • 1
  1. 1.Institute of Cartography and GeoinformaticsUniversity of HannoverGermany

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