Identifying the Geographical Scope of Prohibition Signs

  • Konstantin HopfEmail author
  • Florian Dageförde
  • Diedrich WolterEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9368)


Prohibition signs warn of actions considered dangerous or annoying. Typically, these signs are located near the beginning of their scope, but knowledge about applicable prohibitions is important at any place within the scope. We developed an automated method to determine the scope of signs, aiming to support volunteered geographic information (VGI) applications that wish to capture prohibitions. In this paper we investigate the problem of computing the scope of geo-referenced signs that refer to human outdoor activities using OpenStreetMap (OSM) data. We analyze the problem and discuss the specific challenges faced. From the analysis we derive a symbolic representation that links activities with (OSM) map features, enabling semantic assessment of map features with respect to a prohibition and reasoning to infer its scope. In a comparative evaluation we demonstrate that our spatial-semantic approach significantly outperforms a previous method based on proximity.


Spatial semantics Semantic assessment Prohibition signs Volunteered geographic information (VGI) OpenStreetMap (OSM) 



Financial support of Technnogieallianz Oberfranken (TAO) is gratefully acknowledged. The approach presented in this paper is a significantly revised method previously submitted to a student’s programming competition organized by the German Informatics Society. We thank Alexander Baumgärtner for his contribution to implementing the system.


  1. 1.
    Ballatore, A., Bertolotto, M., Wilson, D.: Geographic knowledge extraction and semantic similarity in OpenStreetMap. Knowl. Inf. Syst. 37(1), 61–81 (2013)CrossRefGoogle Scholar
  2. 2.
    Bennett, B.: Space, time, matter and things. In: Welty, C., Smith, B. (eds.) Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS 2001), pp. 105–116. ACM, Ogunquit (2001)Google Scholar
  3. 3.
    Bennett, B., Mallenby, D., Third, A.: An ontology for grounding vague geographic terms. In: Eschenbach, C., Gruninger, M. (eds.) Proceedings of the 5th International Conference on Formal Ontology in Information Systems (FOIS-08). IOS Press, Amsterdam (2008)Google Scholar
  4. 4.
    Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuAs): Technische Regel für Arbeitsstätten ASR A1.3. Gemeinsames Ministaralblatt (GMBl) 2013(16), 334–347 (2013) (In German)Google Scholar
  5. 5.
    Bundesministerium für Verkehr, Bau und Stadtentwicklung (BMVI), Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (BMU): Verordnung zur Neufassung der Straßenverkehrs-Ordnung (StVO) vom 6. März 2013. Bundesgesetzblatt 2013(12), 367–427 (2013) (In German)Google Scholar
  6. 6.
    Fonseca, F.T., Egenhofer, M.J.: Ontology-driven geographic information systems. In: Proceedings of the 7th ACM International Symposium on Advances in Geographic Information Systems, pp. 14–19. ACM (1999)Google Scholar
  7. 7.
    Janowicz, K., Raubal, M., Kuhn, W.: The semantics of similarity in geographic information retrieval. J. Spat. Inf. Sci. 2011(2), 29–57 (2011)Google Scholar
  8. 8.
    Kanevski, M., Pozdnoukhov, A., Timonin, V.: Machine learning for spatial environmental data: theory, applications, and software. EPFL press, Lausanne (2009)CrossRefGoogle Scholar
  9. 9.
    Kurata, Y.: The 9\(^ \text{+ } \)-Intersection: a universal framework for modeling topological relations. In: Cova, T.J., Miller, H.J., Beard, K., Frank, A.U., Goodchild, M.F. (eds.) GIScience 2008. LNCS, vol. 5266, pp. 181–198. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  10. 10.
    Penn, A., Turner, A.: Space syntax based agent simulation. In: 1st International Conference on Pedestrian and Evacuation Dynamics (2001)Google Scholar
  11. 11.
    Samsonov, P., Tang, X., Schöning, J., Kuhn, W., Hecht, B.: You cant smoke here: Towards support for space usage rules in locationaware technologies. Technical report 14–022, University of Minnesota (2014)Google Scholar
  12. 12.
    Schwering, A.: Approaches to semantic similarity measurement for geo-spatial data: a survey. Trans. GIS 12(1), 5–29 (2008)CrossRefGoogle Scholar
  13. 13.
    Ulmer, A., Halatsch, J., Kunze, A., Müller, P., Van Gool, L.: Procedural design of urban open spaces. Proceed. eCAADe 25, 351–358 (2007)Google Scholar
  14. 14.
    S̆egvic̆, S., Brkić, K., Kalafatić, Z., Pinz, A.: Exploiting temporal and spatial constraints in traffic sign detection from a moving vehicle. Mach. Vis. Appl. 25(3), 649–665 (2014)CrossRefGoogle Scholar
  15. 15.
    Van Hage, W.R., Wielemaker, J., Schreiber, G.: The space package: Tight integration between space and semantics. Trans. GIS 14(2), 131–146 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Information Systems and Applied Computer ScienceUniversity of BambergBambergGermany

Personalised recommendations