A Stochastic Method for the Generation of Optimized Building Layouts Respecting Urban Regulations

  • Shuang HeEmail author
  • Julien Perret
  • Mickaël Brasebin
  • Mathieu Brédif
Part of the Advances in Geographic Information Science book series (AGIS)


The development in an urban area normally has to obey planning regulations. In France, such regulations are specified in local urban planning schemes (LUPS or PLU in French) defining the right to build at municipal or inter-municipal level. Many aspects are concerned in a PLU. We address to the spatial aspect defining the rules for building development. Since these rules are stated in technical documents, it’s not easy to comprehend or to assess their impacts. Driven by such issues, we propose to generate 3D building layouts that comply with the rules and have optimized indicators (e.g. floor area ratio), which is optional but useful. A building layout is a configuration of a number of buildings with various shapes (simplified as 3D boxes in this work). Thus, it can be seen as a realization of a marked point process (MPP) of 3D boxes, whose probability distribution can be defined through Gibbs energy with regard to a reference process. Its energy component reflects the compliance with the PLU rules in our problem. By maximizing this probability the optimal building layout can be found. The optimization task is realized by trans-dimensional simulated annealing (TDSA) coupled with a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler. Several common types of the French PLU rules are studied and modeled into energy terms, and a case study is conducted to validate our approach.


Urban planning Urban modelling Scale Building layout 3D 


  1. Bao F, Yan D-M, Mitra NJ, Wonka P (2013) Generating and exploring good building layouts. ACM Trans Graph 32Google Scholar
  2. Brasebin M (2014). Les données géographiques 3D pour simuler l’impact de la règlementation urbaine sur la morphologie du bâti., Université Paris-EstGoogle Scholar
  3. Brasebin M, Perret J, Haëck C (2011) Towards a 3D geographic information system for the exploration of urban rules: application to the French local urban planning schemes. In: 28th urban data management symposium (UDMS 2011), Delft, NetherlandsGoogle Scholar
  4. Brédif M, Tournaire O (2012) Librjmcmc: an open-source generic C++ library for stochastic optimization. The XXII congress of the international society of photogrammetry and remote sensing, Melbourne, AustraliaGoogle Scholar
  5. Coello Coello CA (2010) Constraint-handling techniques used with evolutionary algorithms. In: Proceedings of the 12th annual conference companion on genetic and evolutionary computation. ACM, Portland, Oregon, USAGoogle Scholar
  6. Coors V, Hünlich K, On G (2009) Constraint-based generation and visualization of 3D city models. In: 3rd International Workshop on 3D Geo-Information. Seoul, Korea, pp 365–378Google Scholar
  7. El Makchouni M (1987) Un système graphique intelligent d’aide à la conception des plans d’occupation des sols: SYGRIPOS. In: 12th urban data management symposium, Blois, FranceGoogle Scholar
  8. Frazer J (1995) An evolutionary architecture. Architectural AssociationGoogle Scholar
  9. Kämpf JH, Montavon M, Bunyesc J, Bolliger R, Robinson D (2010) Optimisation of buildings’ solar irradiation availability. Sol Energy 84:596–603CrossRefGoogle Scholar
  10. Laurini R, Vico F (1999) 3D symbolic visual simulation of building rule effects in urban master plans. In: The second international workshop on urban 3d/multi-media mapping (UM3’99), JapanGoogle Scholar
  11. Métral C, Falquet G, Cutting-Decelle A (2009) Towards semantically enriched 3D city models: an ontology-based approach. GeoWeb, VancouverGoogle Scholar
  12. Murata M (2004) 3D-GIS application for urban planning based on 3D city model. In: 24th annual ESRI international user conferenceGoogle Scholar
  13. Parish YIH, Müller P (2001) Procedural modeling of cities. In: Proceedings of the 28th annual conference on computer graphics and interactive techniques. ACM, New York, NY, USAGoogle Scholar
  14. Perret J, Curie F, Gaffuri J, Ruas A (2010) A multi-agent system for the simulation of urban dynamics. In: 10th European conference on complex systems (ECCS 2010), Lisbon, PortugalGoogle Scholar
  15. Rittel HWJ, Webber MM (1973) Dilemmas in a general theory of planning. Policy Sci 4:155–169CrossRefGoogle Scholar
  16. Ruas A, Perret J, Curie F, Mas A, Puissant A, Skupinski G, Badariotti D, Weber C, Gancarski P, Lachiche N, Lesbegueries J, Braud A (2011) Conception of a GIS-platform to simulate urban densification based on the analysis of topographic data. Lect Notes Geogr Cartography. Springer, Heidelberg 413–430Google Scholar
  17. Salamon P, Sibani P, Frost R (2002) Facts, conjectures, and improvements for simulated annealing. Soc Ind Appl MathGoogle Scholar
  18. Singh HK, Isaacs A, Ray T, Smith W (2008) A simulated annealing algorithm for single objective trans-dimensional optimization problems. In: Eighth international conference on hybrid intelligent systems, 2008. HIS’08. School of Aerospace, University of New South Wales, Canberra, ACTGoogle Scholar
  19. Talton JO, Lou Y, Lesser S, Duke J, Měch R, Koltun V (2011) Metropolis procedural modeling. ACM Trans Graph 30Google Scholar
  20. Tournaire O, Brédif M, Boldo D, Durupt M (2010) An efficient stochastic approach for building footprint extraction from digital elevation models. ISPRS J Photogram Remote Sens 65:317–327CrossRefGoogle Scholar
  21. Turkienicz B, Gonçalves BB, Grazziotin P (2008) CityZoom: a visualization tool for the assessment of planning regulations. Int J Architectural Comput 6:79–95CrossRefGoogle Scholar
  22. van Lieshout MNM (2000) Markov point processes and their applications. Imperial College Press, LondonCrossRefGoogle Scholar
  23. Vanegas CA (2013) Modeling the appearance and behavior of urban spaces. Purdue UniversityGoogle Scholar
  24. Wonka P, Wimmer M, Sillion F, Ribarsky W (2003) Instant architecture. ACM Trans Graph 22:669–677CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuang He
    • 1
    Email author
  • Julien Perret
    • 1
  • Mickaël Brasebin
    • 1
  • Mathieu Brédif
    • 2
  1. 1.IGN, COGITUniversité Paris-EstSaint MandéFrance
  2. 2.IGN, MATISUniversité Paris-EstSaint MandéFrance

Personalised recommendations