Inferring the Scale of OpenStreetMap Features

  • Guillaume Touya
  • Andreas Reimer
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Traditionally, national mapping agencies produced datasets and map products for a low number of specified and internally consistent scales, i.e. at a common level of detail (LoD). With the advent of projects like OpenStreetMap, data users are increasingly confronted with the task of dealing with heterogeneously detailed and scaled geodata. Knowing the scale of geodata is very important for mapping processes such as for generalization of label placement or land-cover studies for instance. In the following chapter, we review and compare two concurrent approaches at automatically assigning scale to OSM objects. The first approach is based on a multi-criteria decision making model, with a rationalist approach for defining and parameterizing the respective criteria, yielding five broad LoD classes. The second approach attempts to identify a single metric from an analysis process, which is then used to interpolate a scale equivalence. Both approaches are combined and tested against well-known Corine data, resulting in an improvement of the scale inference process. The chapter closes with a presentation of the most pressing open problems.


Generalization Scale Data quality Level of detail OpenStreetMap 


  1. Barron C, Neis P, Zipf A (2013) Towards intrinsic quality analysis of OpenStreetMap datasets. In: Online proceedings of the international workshop on action and interaction in volunteered geographic information (ACTIVITY). AgileGoogle Scholar
  2. Bereuter P, Weibel R (2013) Real-time generalization of point data in mobile and web mapping using quadtrees. Cartogr Geogr Inf Sci 40(4):271–281CrossRefGoogle Scholar
  3. Biljecki F, Ledoux H, Stoter J, Zhao J (2014) Formalisation of the level of detail in 3D city modelling. Comput Environ Urban Syst 48:1–15CrossRefGoogle Scholar
  4. Chave J (2013) The problem of pattern and scale in ecology: what have we learned in 20 years? Ecol Lett 16:4–16CrossRefGoogle Scholar
  5. Clark JH (1976) Hierarchical geometric models for visible surface algorithms. Commun ACM 19(10):547–554CrossRefGoogle Scholar
  6. Dutton G (1999) Scale, sinuosity and point selection in digital line generalization. Cartogr Geogr Inf Sci 26(1):33–53CrossRefGoogle Scholar
  7. Feick R, Robertson C (2014) A multi-scale approach to exploring urban places in geotagged photographs. Comput Environ Urban Syst (in press)Google Scholar
  8. Figueira J, Greco S, Ehrogott M (eds) (2005a) Multiple criteria decision analysis: state of the art surveys of international series in operations research and management science, vol 78. Springer, HeidelbergGoogle Scholar
  9. Figueira J, Mousseau V, Roy B (2005b) ELECTRE methods. In: Figueira J, Greco S, Ehrogott M (eds) Multiple criteria decision analysis: state of the art surveys. Springer, Heidelberg, pp 133–162Google Scholar
  10. Freitag U (1962) Der Kartenmaßstab—Betrachtungen über den Maßstabsbegriff in der Kartographie. Kartographische Nachrichten, Heft 5,12. Jahrgang, Gütersloh, pp 134–146Google Scholar
  11. Girres J-F (2011) An evaluation of the impact of cartographic generalisation on length measurement computed from linear vector databases. In: Proceedings of 25th international cartographic conference (ICC’11), Paris, France. ICAGoogle Scholar
  12. Girres J-F, Touya G (2010) Quality assessment of the french OpenStreetMap dataset. Trans GIS 14(4):435–459CrossRefGoogle Scholar
  13. Goodchild MF, Li L (2012) Assuring the quality of volunteered geographic information. Spat Stat 1:110–120CrossRefGoogle Scholar
  14. Grosso E, Perret J, Brasebin M (2012) GEOXYGENE: an interoperable platform for geographical application development. In: Innovative software development in GIS (Chapter 3), Wiley, New York, pp 67–90Google Scholar
  15. Haggett P, Chorley RJ, Stoddart DR (1965) Scale standards in geographical research: a new measure of areal magnitude. Nature 205:844–847CrossRefGoogle Scholar
  16. Haklay M (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ Plann B: Plann Des 37(4):682–703CrossRefGoogle Scholar
  17. Haklay M, Basiouka S, Antoniou V, Ather A (2010) How many volunteers does it take to map an area well? The validity of linus’s law to volunteered geographic information. Cartogr J 47(4):315–322CrossRefGoogle Scholar
  18. Keßler C, de Groot RT (2013) Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap. In: Vandenbroucke D, Bucher B, Crompvoets J (eds) Geographic information science at the heart of Europe. Lecture notes in geoinformation and cartography. Springer International Publishing, New York, pp 21–37Google Scholar
  19. Kolbe TH (2009) Representing and exchanging 3D city models with CityGML. In: Lee J, Zlatanova S (eds) 3D Geo-information sciences. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 15–31Google Scholar
  20. Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73(6):1943–1967CrossRefGoogle Scholar
  21. Mondzech J, Sester M (2011) Quality analysis of OpenStreetMap data based on application needs. Cartogr Int J Geogr Inf Geovisual 46(2):115–125Google Scholar
  22. Mooney P, Corcoran P (2012) The annotation process in OpenStreetMap. Trans GIS 16(4):561–579CrossRefGoogle Scholar
  23. Reimer A, Kempf C, Rylov M, Neis P (2014) Assigning scale equivalencies to OpenStreetMap polygons. In: Proceedings of AutoCarto international symposium on automated cartography 2014 (accepted)Google Scholar
  24. Rosenholtz R, Li Y, Nakano L (2007) Measuring visual clutter. J Vis 7(2):17Google Scholar
  25. Sester M, Jokar Arsanjani J, Klammer R, Burghardt D, Haunert J-H (2014) Integrating and generalising volunteered geographic information. In: Burghardt D, Duchêne C, Mackaness W (eds) Abstracting geographic data in a data rich world. Springer, Berlin, pp 119–155Google Scholar
  26. Skarlatidou A, Haklay M, Cheng T (2011) Trust in web GIS: the role of the trustee attributes in the design of trustworthy web GIS applications. Int J Geogr Inf Sci 25(12):1913–1930CrossRefGoogle Scholar
  27. Steinhardt U (1999) Die Theorie der geographischen Dimensionen in der Angewandten Landschaftsökologie. In: Schneider-Sliwa et al (eds) Angewandte Landschaftsökologie. Springer, BerlinGoogle Scholar
  28. Stoter J, Burghardt D, Duchêne C, Baella B, Bakker N, Blok C, Pla M, Regnauld N, Touya G, Schmid S (2009) Methodology for evaluating automated map generalization in commercial software. Comput Environ Urban Syst 33(5):311–324CrossRefGoogle Scholar
  29. Sudgen D, Hamilton P (1971) Scale, systems and regional geography. Area 3(3):139–144Google Scholar
  30. Surowiecki J (2004) The wisdom of crowds. Anchor BooksGoogle Scholar
  31. Töpfer F, Pillewizer W (1966) The principle of selection. Cartogr J 3:10–16CrossRefGoogle Scholar
  32. Töpfer F (1979) Kartographische generalisierung, 2nd edn. VEB Hermann Haack, GothaGoogle Scholar
  33. Touya G (2012) What is the level of detail of OpenStreetMap? In: Workshop on role of volunteered geographic information in advancing science: quality and credibility. Columbus (Ohio), USAGoogle Scholar
  34. Touya G, Brando-Escobar C (2013) Detecting level-of-detail inconsistencies in volunteered geographic information data sets. Cartogr Int J Geogr Inf Geovisual 48(2):134–143Google Scholar
  35. Touya G, Baley M (2014) Harmonizing level of details in OpenStreetMap based maps. In: Duckham M, Stewart K, Pebesma E (eds) Proceedings of GIScience 2014—Poster session, Vienna, AustriaGoogle Scholar
  36. Zielstra D, Zipf A (2010) A comparative study of proprietary geodata and volunteered geographic information for Germany. In: Proceedings of 13th Agile international conference on geographic information science. Guimaraes, PortugalGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.COGITInstitut national de l’information géographique et forestièreParisFrance
  2. 2.Department of GeographyHeidelberg UniversityHeidelbergGermany

Personalised recommendations