A New Algorithm for 3D Isovists

  • Wassim Suleiman
  • Thierry Joliveau
  • Eric Favier
Part of the Advances in Geographic Information Science book series (AGIS)


Isovist or vision field computing is an interesting topic with many applications in different fields: security, wireless network design, or landscape management. In all existing solutions, a 3D environment appears to be the most challenging task and few solutions exist for detecting the obstacles that limit the vision field. In this paper a new algorithm is presented for isovist calculation that can detect all objects, which block the sight in a 2D and 3D environment. Then, a demonstration with GIS data is given and some visibility indices are also presented.


Field of vision GIS Photometry Isovist Information visualization Ray tracing Virtual reality Visualization techniques and methodologies Space syntax 


  1. Ashihara Y (1984) The aesthetic townscape. MIT Press, Cambridge, pp 195–139Google Scholar
  2. Batty M, Jiang B (1999) Multi-agent simulation: new approaches to exploring space-time dynamics in GIS.
  3. Benedict ML (1979) To take hold of space: isovists and isovists fields. Environ Plann B 6:47–65CrossRefGoogle Scholar
  4. Bentley JL, Ottmann TA (1979) Algorithms for reporting and counting geometric intersections. IEEE Trans Comput C-28:643–647Google Scholar
  5. Bilsen V (2009) How can serious games benefit from 3D visibility analysis? Presented at the international simulation and gaming association conference, SingaporeGoogle Scholar
  6. Bilsen V (2010) 3D visibility analysis in virtual learning environments and interactive and digital media. Presented at the interactive& digital media for education in virtual learning environment, New YorkGoogle Scholar
  7. Boehm J, Haala N, Kapusy P (2002) Automated appearance-based building detection in terrestrial images. In: ISPRS commission v symposium, international archives on photogrammetry and remote sensing, vol 34, pp 491–495Google Scholar
  8. Brossard T, Joly D, Tourneux F (2008) Modélisation opérationnelle du paysage. Paysage et information géographique, Lavoisier, pp 117–137Google Scholar
  9. Christenson M (2010) Registering visual permeability in architecture: isovists and occlusion maps in AutoLISP. Environ Plann B Plann Des 37:1128–1136CrossRefGoogle Scholar
  10. Cipolla Drummond (2000) Vision algorithms: theory and practice. Springer, BerlinGoogle Scholar
  11. Conroy Dalton R, Bafna S (2003) The syntactical image of the city: a reciprocal definition of spatial elements and spatial syntaxes.
  12. David P, Dementhon D, Duraiswami R, Samet H (2004) SoftPOSIT: simultaneous pose and correspondence determination. Int J Comput Vision 59(3):259–284Google Scholar
  13. De Floriani L, Magillo P (1994) Abstract visibility algorithms on triangulated digital terrain models. Int J Geogr Inform Syst 8(1):13–41Google Scholar
  14. Do EY-L (1994a) Design and description of form—using tool command language Tk/Tcl to visualize isovist by lighting and shadow casting analogyGoogle Scholar
  15. Do EY-L (1994b) Isovist calculation in AutoCADGoogle Scholar
  16. Do EY-L (1995) Visual analysis through Isovist—building a computation toolGoogle Scholar
  17. Do EY-L (1997) Tools for visual and spatial analysis of CAD models. CAAD futures 1997 conference, pp 373–388Google Scholar
  18. Drummond T, Cipolla R (2002) Real-time visual tracking of complex structures. IEEE Trans Pattern Anal Machine Intell 24:932–946CrossRefGoogle Scholar
  19. Fisher-Gewirtzman D, Shach Pinsly D, Wagner IA, Burt M (2005) View-oriented three-dimensional visual analysis models for the urban environment. Urban Des Int 10:23–37Google Scholar
  20. Fishman J, Haverkort H, Toma L (2009) Improved visibility computation on massive grid terrains. Presented at the (2009)Google Scholar
  21. Floriani LD, Magillo P (2003) Algorithms for visibility computation on terrains: a survey. Environ Plann B Plann Des 30:709–728CrossRefGoogle Scholar
  22. Franklin WR, Ray CK (1994) Higher isn’t necessarily better: visibility algorithms and experiments. In: Advances in GIS research: sixth international symposium on spatial data handling, vol 5, pp 751–770Google Scholar
  23. Gibson JJ (1983) The senses considered as perceptual systems. Greenwood Press Reprint, WestportGoogle Scholar
  24. Hillier B, Hanson J (1984) The social logic of space. Cambridge University Press, CambridgeGoogle Scholar
  25. Ittelson W (1960) Visual space perception, vol. 212. Springer, New York, pp. 6: Science 133:1241–1242 (1961)Google Scholar
  26. Lake IR, Lovett AA, Bateman IJ, Day B (2000) Using GIS—and large-scale digital data to implement hedonic pricing studies. Int J Geog Inform Sci 14:521CrossRefGoogle Scholar
  27. Lynch K (1976) What time is this place? The MIT Press, CambridgeGoogle Scholar
  28. Morello E, Ratti C (2009) A digital image of the city: 3D isovists in Lynch’s urban analysis. Environ Plann B Plann Des 36:837–853CrossRefGoogle Scholar
  29. Ortegón-Aguilar J, Bayro-Corrochano E (2006) Lie algebra and system identification techniques for 3D rigid motion estimation and monocular tracking. J Math Imaging Vision 25:173–185CrossRefGoogle Scholar
  30. PUTRA SY (2005) GIS-based 3D volumetric visibility analysis and spatial and temporal perceptions of urban spaceGoogle Scholar
  31. Putra SY, Yang PP-J (2005) Analysing mental geography of residential environment in Singapore using GIS-based 3D visibility, analysisGoogle Scholar
  32. Pyysalo U, Oksanen J, Sarjakoski T (2009) Viewshed analysis and visualization of landscape voxel models. In: 24th international cartographic conference, Santiago, ChileGoogle Scholar
  33. Rana S (2006) Isovist analyst: an arcview extension for planning visual surveillance.
  34. Rosenberg Y, Werman M (1998) Real-time object tracking from a moving video camera: a software approach on a PC. In: IEEE workshop on applications of computer vision, pp 238–239Google Scholar
  35. Sourimant G, Morin L, Bouatouch K, De Rennes (2009) GPS, GIS and video registration for building reconstruction.
  36. Suleiman W, Joliveau T, Favier E (2011) 3D urban visibility analysis with vector GIS data. Presented at the GISRUK 2011, University of Portsmouth, UK, pp 27–29, 26 April 2011Google Scholar
  37. Turner A, Doxa M, O’Sullivan D, Penn A (2001) From isovists to visibility graphs: a methodology for the analysis of architectural space. Environ Plann B 28:103–121CrossRefGoogle Scholar
  38. Van Kreveld M (1996) Variations on sweep algorithms: efficient computation of extended viewsheds and class intervals. In: Proceedings of the 7th international symposium on spatial data handling, pp 13–15Google Scholar
  39. Zhao C, Shi W, Deng Y (2005) A new Hausdorff distance for image matching. Pattern Recogn Lett 26:581–586CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wassim Suleiman
    • 1
  • Thierry Joliveau
    • 1
  • Eric Favier
    • 2
  1. 1.ISTHME-EVS CNRS UMR 5600Université Jean Monnet-Saint-EtienneSaint-EtienneFrance
  2. 2.University of Lyon-Ecole nationale d’ingénieurs de Saint-Etienne DIPI (ENISE)Saint-EtienneFrance

Personalised recommendations