Advertisement

Interpretation of Geographic Vector-Data in Practice

  • Jurgen den Hartog
  • Bernardus T. Holtrop
  • Marlies E. de Gunst
  • Ernst-Peter Oosterbroek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

In this paper an application is presented for rule-based polygon classification based on geographic vector data. Early results from the evaluation by the Dutch Cadastre are given. From the experiments we conclude that a rule-based approach to interpretation of vector data leads to a speed-up of a factor 2 while maintaining a similar classification performance when compared with manual classification.

Keywords

Automatic Classification Topological Relation Manual Classification Reasoning Engine Vector Label 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K.-H. Anders, D. Fritsch, “Automatic interpretation of digital maps for data revision”, ISPRS, Int. Archiv. of Photogr, and Remote Sensing, Vol. 31/4, Vienna, 1996, pp. 90–94Google Scholar
  2. 2.
    J. Alemany et al. “Interpretation of paper-based maps”, SPIE Appl. of Digital Image Processing X, volume 829, pp. 125–137, 1987Google Scholar
  3. 3.
    D. Antoine. “CIPLAN, a model-based system with original features for understanding French plats”, First int. Conf. On Document Analysis and Recognition (Saint Malo), volume 2, pp. 647–655, 1991Google Scholar
  4. 4.
    ESRI, Data Conversion and Regions, ARC/INFO Version 7 User Manual, Environmental Systems Research Institute, Redlands, USA, pp. 92–96, 1994Google Scholar
  5. 5.
    Joseph C. Giarratano, The CLIPS User’s guide, CLIPS Version 6.0, NASA Lyndon B. Johnson Space Center, Software Technology Branch, May 28, 1993Google Scholar
  6. 6.
    K. J. Goodson and P. H. Lewis, “A knowledge-based line recognition systemle”, Pattern Recognition Letters, 11(4), pp. 295–304, 1990zbMATHCrossRefGoogle Scholar
  7. 7.
    J. E. den Hartog, “A framework for knowledge-based map interpretationln”, PhD-thesis, Delft University of Technology, Fac. of Electrical Engineering, 88 p., 1995Google Scholar
  8. 8.
    R. Laurini, F. Milleret-Raffort, “Topological reorganization of inconsistent geographical databases: a step towards their certification”, Computer and Graphics, Vol. 18, No. 6, pp. 803–813, 1994CrossRefGoogle Scholar
  9. 9.
    R. Laurini and D. Thompson, “Fundamentals of spatial information systems”, The A.P.I.C. Series, No. 37, Academic Press, 1992Google Scholar
  10. 10.
    Karttakeskus., Fingis User Manual, version 3.85, Technical report Karttakeskus, Helsinki, Finland, 1994Google Scholar
  11. 11.
    M. Molenaar, “An introduction to the theory of spatial object modelling for GIS”, Taylor & Francis, London, 1998Google Scholar
  12. 12.
    P. van der Molen, “Inside the dutch cadastre”, GIS Europe, (10), pp. 28–30, 1996Google Scholar
  13. 13.
    P. van Oosterom and T. Vijlbrief, “The spatial location code”, Proceedings of the 7th International Symposium on Spatial Data Handling, Delft, The Netherlands, 1996Google Scholar
  14. 14.
    S. Suharto and D. M. Vu, “Computerized cartographic work for censuses and surveys”, UNFPA TSS/CST workshop on Data Collection, Processing, Dissemination and Utilization, New York, 1995Google Scholar
  15. 15.
    B. Žalik, “A topology construction from line drawings using a uniform plane subdivision technique”, Computer-Aided Design 31, pp. 335–348, 1999zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jurgen den Hartog
    • 1
  • Bernardus T. Holtrop
    • 1
  • Marlies E. de Gunst
    • 2
  • Ernst-Peter Oosterbroek
    • 2
  1. 1.TNO Institute of Applied PhysicsDelftThe Netherlands
  2. 2.Cadastre and Public RegistersApeldoornThe Netherlands

Personalised recommendations