Advertisement

Preparing Simplified 3D Scenes of Multiple LODs of Buildings in Urban Areas Based on a Raster Approach and Information Theory

  • Alexey Noskov
  • Yerach Doytsher
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

We have developed a method for the simplification of the footprints of 2D buildings based on a rasterisation process. The rasterisation is processed within quarters and the urban area is subdivided into quarters based on natural contours such as roads and water objects and not on straight geometric lines (the common subdivision approach to orthogonal tiles). Quarters were organised into a hierarchical model, according to the gaps between the quarters and the stages of the clustering process, using Kohonen’s self-organising maps. Each degree of simplification (generalisation) corresponds to some level of hierarchy. Information theory was used to estimate the amount of the 2D generalisation of building footprints. Simplified building footprints were extruded for the compilation of a 3D urban perspective from multiple levels of detail (LODs) The entropies of 3D scenes for each quarter of the hierarchy and each LOD were compared in order to define the level of detail to be used in the final 3D scene.

Keywords

Voronoi Region Buffer Width Single Building Voronoi Polygon Building Footprint 
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. Battersby SE, Keith C (2003) Quantifying Information in Line Generalisation. Department of Geography, University of California at Santa Barbara, CA 93106 USA, Proceedings of the 21st International Cartographic Conference (ICC), Durban, South AfricaGoogle Scholar
  2. Bjorke JT (1996) Framework for entropy-based map evaluation. Cartography and Geographical Information Systems 23: 78–95Google Scholar
  3. Clarke, Keith C, Battersby SE (2001) The Coordinate Digit Density function and Map Information Content Analysis. Proceedings of the American Congress on Surveying and Mapping Annual Meeting, Las Vegas, NVGoogle Scholar
  4. Forberg A (2007) Generalisation of 3D Building Data Based on a Scale-Space Approach. ISPRS Journal of Photogrammetry & Remote Sensing 62:104–111Google Scholar
  5. Glander T, Döllner J (2009) Abstract representations for interactive visualization of virtual 3D city models. Computers, Environment and Urban Systems 33: 375–387Google Scholar
  6. Guercke R, Götzelmann T, Brenner C, Sester M (2011) Aggregation of LoD 1 building models as an optimization problem. ISPRS Journal of Photogrammetry and Remote Sensing 66: 209–222Google Scholar
  7. He S, Moreau G, Martin J (2012) Footprint-Based 3D Generalisation of Building Groups for Virtual City. GEOProcessing 2012: The Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services, Valencia, SpainGoogle Scholar
  8. Kada M (2002) Automatic Generalisation of 3D Building Models. Proceedings of the Joint International Symposium on Geospatial Theory, Processing and Applications, Ottawa, CanadaGoogle Scholar
  9. Knopfli R (1983) Communication theory and generalisation. In: Graphic Communication and Design in Contemporary Cartography, ed. by D.R.F. Taylor, 177–218, New York, John Wilyey & Sons LtdGoogle Scholar
  10. Li Z, Yan H, Ai T, Chen J (2004) Automated building generalisation based on urban morphology and Gestalt theory. International Journal of Geographical Information Science 18 (5): 513–534 16Google Scholar
  11. Li Z, Huang P, (2002) Quantitative measures for spatial information of maps. International Journal of Geographic Information Science 16(7): 699–709Google Scholar
  12. Neumann J (1994) The topological information content of a map/An attempt at rehabilitation of information theory incartography. Cartographica, 31(1): 26–34.Google Scholar
  13. Noskov A, Doytsher Y (2013a) Urban perspectives: A raster-based approach to 3D generalization of groups of buildings. GEOProcessing 2013: The Fifth International Conference on Advanced Geographic Information Systems, Applications, and Services, Nice, FranceGoogle Scholar
  14. Noskov A, Doytsher Y (2013b) Hierarchical quarters model approach toward 3D raster based generalization of urban environments. International Journal on Advances in Software 6 (3&4): 343–353Google Scholar
  15. Shannon CE (1948) A mathematical theory of communication. The Bell System Technical Journal, 27: 379–423 & 623–656Google Scholar
  16. Sukhov VI (1970) Application of information theory in generalization of map contents. International Yearbook of Cartography, 10: 41–47Google Scholar
  17. Thiemann F (2002) Generalization of 3D Building Data. Proceedings of Symposium of Geospatial Theory, Processing and Applications, Ottawa, CanadaGoogle Scholar
  18. Trapp M, Glander T, Buchholz H (2008) 3D Generalization Lenses for Interactive Focus + Context Visualization of Virtual City Models. Proceedings of the 12th International Conference Information Visualization, London, UKGoogle Scholar
  19. Xie J, Zhang L, Li J (2012) Automatic simplification and visualization of 3D urban building models. International Journal of Applied Earth Observation and Geoinformation 18: 222–231Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Mapping and Geo-information EngineeringTechnion—Israel Institute of TechnologyHaifaIsrael

Personalised recommendations