Advertisement

Automatic Map Retrieval and Map Interpretation in the Internet

Chapter
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

The Internet contains huge amounts of maps representing almost every part of the Earth in many different scales and map types. However, this enormous quantity of information is completely unstructured and it is very difficult to find a map of a specific area and with certain content, because the map content is not accessible by search engines in the same way as web pages. However, searching with search engines is at the moment the most effective way to retrieve information in the Internet and without search engines most information would not be findable. In order to overcome this problem, methods are needed to search automatically for maps in the Internet and to make the implicit information of maps explicit so that machines can process it. In this paper we discuss how maps can be found automatically in the Internet and moreover, how the content of maps can be interpreted automatically.

Keywords

Interpretation Data mining Internet Retrieval Databases 

References

  1. Agarwal A, Skupin A (eds) (2008) Self-organizing maps: applications in geographic information science. Wiley, West SussexGoogle Scholar
  2. Anders KH (2003) A hierarchical graph-clustering approach to find groups of objects. In: ICA commission on map generalization, technical paper at the fifth workshop on progress in automated map generalization, IGN, Paris, published on CD-ROM, Internet access: http://www.geo.unizh.ch/ICA/docs/paris2003/papers03.html. Accessed 5 Jan 2012
  3. Datta R, Joshi D, Li J, Wang J (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2), Article 5, p 60.Google Scholar
  4. Frawley W, Piatetsky-Shapiro G, Matheus C (1991) Knowledge discovery in databases: an overview. In: Piatetsky-Shapiro G, Frawley W (eds) Knowledge discovery in databases. AAAI/MIT Press, Menlo Park, p 27Google Scholar
  5. Funkhouser T, Min P, Kazhdan M, Chen J (2003) A search engine for 3D models. ACM Trans Graph 22(1):83–105CrossRefGoogle Scholar
  6. Graeff B, Carosio A (2002) Automatic interpretation of raster-based topographic maps by means of queries. FIG XXII international congress Washington, published on CD-ROM, Internet access: http://www.fig.net/pub/fig_2002/Ts3-10/TS3_10_graeff_carosio.pdf. Accessed 5 Jan 2012
  7. Heinzle F, Sester M (2004) Derivation of implicit information from spatial data sets with data mining. Int Arch Photogrammetry Remote Sens 35(Part B4):335–340.Google Scholar
  8. Heinzle F, Anders KH, Sester M (2007) Automatic detection of pattern in road networks-methods and evaluation. In: Proceeding of joint workshop visualization and exploration of geospatial data, vol XXXVI-4/W45, published on CD-ROM, Internet access: http://tiny.cc/FEyb7. Accessed 5 Jan 2012
  9. Jones JB, Abdelmoty AI, Finch D, Fu D, Vaid S (2004) The SPIRIT spatial search engine: architecture, ontologies and spatial indexing. In: Proceedings of Geographic information science: third international conference, GIScience 2004, Adelphi, MD, USA, 20–23 Oct 2004.Google Scholar
  10. Jones C, Purves R, Clough P, Joho H (2008) Modeling vague places with knowledge from the Web. IJGIS 22(10):1045–1065Google Scholar
  11. Kohonen T (1982) Clustering, taxonomy, and topological maps of patterns. In: Proceedings of international conference on pattern recognition (ICPR), Washington, IEEE Computer Soc. Press, pp 114–128.Google Scholar
  12. Lüscher P, Weibel R, Mackaness A (2008) Where is the terraced house? On the use of ontologies for recognition of urban concepts in cartographic databases. In: Ruas A, Christopher G (eds) Headway in spatial data handling. Lecture notes in geoinformation and cartography, Springer, Berlin, pp 449–466CrossRefGoogle Scholar
  13. Mackaness W, Edwards G (2002) The importance of modeling pattern and structure in automated map generalisation. In: Proceedings of joint workshop on multi-scale representations of spatial data, Ottawa, Canada, published on CD-ROM, Internet access: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.1311&rep=rep1&type=pdf. Accessed 5 Jan 2012
  14. Miller HJ, Han J (eds) (2009) Geographic data mining and knowledge discovery, 2nd edn. Taylor and Francis, New YorkGoogle Scholar
  15. Schleinkofer MF (2007) Wissensbasierte Unterstützung zur Erstellung von Produktmodellen im Baubestand. Technische Universität München, DissertationGoogle Scholar
  16. Sester M (2000) Knowledge acquisition for the automatic interpretation of spatial data. Int J Geog Inf Sci 14(1):1–24CrossRefGoogle Scholar
  17. Sezgin TM, Davis R (2005) HMM-based efficient sketch recognition. In: Proceedings of the international conference on intelligent user interfaces (IUI’05), ACM Press, pp 281–283.Google Scholar
  18. Singh A, Singh K (2010) Faster and efficient web crawling with parallel migrating web crawler. IJCSI Int J Comput Sci, Issues 7(3), no. 11:28–32.Google Scholar
  19. Steinhauer JH, Wiese T, Freksa C, Barkowsky T (2001) Recognition of abstract regions in cartographic maps. In: Proceedings of the international conference on spatial information theory: foundations of geographic information science, pp 306–321.Google Scholar
  20. Viglino JM, Pierrot-Deseilligny M (2003) A vector approach for automatic interpretation of the french cadastral map. In: Proceedings of the seventh international conference on document analysis and recognition (ICDAR’03), pp 304–309.Google Scholar
  21. Walter V (2008) Automatic interpretation of vector databases with a raster-based algorithm. In: The international archives of the photogrammetry, remote sensing and spatial information sciences 37 (Part B2), pp 175–181.Google Scholar
  22. Walter V, Luo F (2011) Automatic interpretation of digital maps. ISPRS J Photogrammetry Remote Sens 66(4):519–528CrossRefGoogle Scholar
  23. Wuersch M, Egenhofer MJ (2008) Perceptual sketch interpretation. In: Ruas A, Gold C (eds) The 13th international symposium on spatial data handling (SDH 2008). Springer, Montpellier, France, pp 19–38Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute for Photogrammetry, Stuttgart UniversityStuttgartGermany

Personalised recommendations