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International Conference: Beyond Databases, Architectures and Structures

BDAS 2018: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety pp 390–403Cite as

The Use of Minimal Geometries in Automated Building Generalization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 928))

Abstract

As one of the components of automatic mapping, building generalization is one of the most difficult. The complexity of this process is related to the fact that, in addition to the algorithms used to simplify geometric structure, we must also take into account procedures that maintain the topological relations of the neighborhood. Nevertheless, the choice of the correct simplification method is a crucial task. Therefore, this article presents two new simplification algorithms designed by the authors, Area- and Orientation-Maintained Rectangle (AaOMR) and Topological-Diagonal Maxima (TDM). Two new methods and three commonly used ones, Minimum Bounding Rectangle by Width (RbW), Minimum Bounding Rectangle by Area (RbA), and Building Envelope (E) were compared to each other. The research tests of these algorithms cover comparison of several parameters, shifting the centroid, change in area, minimal width and displacement of vertices. Additionally, the proposed algorithms are attached to this article as ready-to-use GIS toolboxes.

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References

  1. Piórkowski, A.: Mysql spatial and postgis-implementations of spatial data standards. Electron. J. Pol. Agric. Univ. - Geodesy Cartogr. 14(1) (2011)

    Google Scholar 

  2. Alexeev, V.V., Bogaevskaya, V.G., Preobrazhenskaya, M.M., Ukhalov, A.Y., Edelsbrunner, H., Yakimova, O.P.: An algorithm for cartographic generalization that preserves global topology. J. Math. Sci. 203(6), 754–760 (2014). https://doi.org/10.1007/s10958-014-2165-8

    Article  MathSciNet  Google Scholar 

  3. ArcGIS: Minimum Bounding Geometry (2018). http://pro.arcgis.com/en/pro-app/tool-reference/data-management/minimum-bounding-geometry.htm

  4. Baas, A.C.: Chaos, fractals and self-organization in coastal geomorphology: simulating dune landscapes in vegetated environments. Geomorphology 48(1–3), 309–328 (2002). http://linkinghub.elsevier.com/retrieve/pii/S0169555X02001873

  5. Chrobak, T., Kozioł, K., Krawczyk, A., Lupa, M., Szombara, S.: Automatyzacja procesu generalizacji wielorozdzielczej bazy danych. Wydawnictwo AGH, Kraków (2013)

    Google Scholar 

  6. Chrobak, T., Lupa, M., Szombara, S., Dejniak, D.: The use of cartographic control points in the harmonization and revision of MRDBs. In: Geocarto International, pp. 1–16 (2017). https://www.tandfonline.com/doi/full/10.1080/10106049.2017.1386721

  7. Clementini, E., Di Felice, P., van Oosterom, P.: A small set of formal topological relationships suitable for end-user interaction. In: Abel, D., Chin Ooi, B. (eds.) SSD 1993. LNCS, vol. 692, pp. 277–295. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56869-7_16

    Chapter  Google Scholar 

  8. Egenhofer, M.J., Sharma, J., Mark, D.: A critical comparison of the 4-intersection and 9-intersection models for spatial relations: formal analysis. In: McMaster, R., Armstrong, M. (eds.) Autocarto 11

    Google Scholar 

  9. Hampe, M., Anders, K.H., Sester, M.: MRDB applications for data revision and real-time generalisation. In: Proceedings of the 21st International Cartographic Conference, August, pp. 10–16 (2003)

    Google Scholar 

  10. Herring, J.R.: Open Geospatial Consortium Inc. Status: OpenGIS \(\textregistered \) Implementation Standard for Geographic information - Simple feature access - Part 2: SQL option. Technical report, v 1.2.1 (2010). https://portal.opengeospatial.org/files/?artifact_id=25354

  11. Li, Z., Yan, H., Ai, T., Chen, J.: Automated building generalization based on urban morphology and Gestalt theory. Int. J. Geograph. Inf. Sci. 18(5), 513–534 (2004). https://www.tandfonline.com/doi/full/10.1080/13658810410001702021

  12. Lupa, M., Kozioł, K.: The use of merging and aggregation operators for MRDB data feeding. Geoinformatica Polonica 12, 17–24 (2013)

    Google Scholar 

  13. Lupa, M., Kozioł, K., Leśniak, A.: An attempt to automate the simplification of building objects in multiresolution databases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 448–459. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18422-7_40

    Chapter  Google Scholar 

  14. Lupa, M., Szombara, S., Chuchro, M., Chrobak, T.: Limits of colour perception in the context of minimum dimensions in digital cartography. ISPRS Int. J. Geo-Inf. 6(9), 276 (2017). http://www.mdpi.com/journal/ijgi. http://www.mdpi.com/2220-9964/6/9/276

  15. McMaster, R.B.: A statistical analysis of mathematical measures for linear simplification. Cartogr. Geogr. Inf. Sci. 13(2), 103–116 (1986). http://openurl.ingenta.com/content/xref?genre=article&issn=1523-0406&volume=13&issue=2&spage=103

  16. Minister of Internal Affairs and Arministration: The regulation of the minister of internal affairs and arministration of 17 november 2011 on topographic object database, geopraphic object database and standard maps. (in polish: Rozporzadzenie ministra spraw wewnȩtrznych i administracji z dnia 17 listopada 2011 r. w sprawie bazy danych obiektów topograficznych oraz bazy danych obiektów ogólnogeograficznych, a także standardowych opracowań kartograficznych), November 2011, dz.U. 2011 nr 279 poz. 1642

    Google Scholar 

  17. Olszewski, R.: Cartographic modelling of terrain relief with the use of computational intelligence methods. Prace Naukowe Politechniki Warszawskiej. Geodezja 46, 3–224 (2009)

    Google Scholar 

  18. Ory, J., Christophe, S., Fabrikant, S.I., Bucher, B.: How do map readers recognize a topographic mapping style? Cartogr. J. 52(2), 193–203 (2015). http://www.tandfonline.com/doi/full/10.1080/00087041.2015.1119459

  19. Papadias, D., Theodoridis, Y.: Spatial relations, minimum bounding rectangles, and spatial data structures. Int. J. Geogr. Inf. Sci. 11(2), 111–138 (1997). http://www.tandfonline.com/doi/abs/10.1080/136588197242428

  20. Raposo, P.: Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation. Cartogr. Geogr. Inf. Sci. 40(5), 427–443 (2013). http://www.tandfonline.com/doi/abs/10.1080/15230406.2013.803707

  21. Regnauld, N.: Contextual building typification in automated map generalization. Algorithmica 30(2), 312–333 (2001). https://doi.org/10.1007/s00453-001-0008-8

    Article  MathSciNet  MATH  Google Scholar 

  22. Sester, M.: Generalization based on least squares adjustment. Int. Arch. Photogramm. Remote Sens. 33, 931–938 (2000). http://elib.uni-stuttgart.de/opus/volltexte/2001/842/

  23. Shoman, W., Gülgen, F.: Centrality-based hierarchy for street network generalization in multi-resolution maps. Geocarto Int. 32(12), 1352–1366 (2017). https://www.tandfonline.com/doi/full/10.1080/10106049.2016.1208683

  24. Spiess, E., Baumgartner, U., Arn, S., Vez, C.: Topographic Maps Map Graphics and Generalisation. Cartographic Publication Series No. 17, Swiss Society of Cartography (2005)

    Google Scholar 

  25. Steiniger, S., Weibel, R.: Relations among map objects in cartographic generalization. Cartogr. Geogr. Inf. Sci. 34(3), 175–197 (2007). http://www.tandfonline.com/doi/abs/10.1559/152304007781697866

  26. Stoter, J., Post, M., van Altena, V., Nijhuis, R., Bruns, B.: Fully automated generalization of a 1:50k map from 1:10k data. Cartogr. Geogr. Inf. Sci. 41(1), 1–13 (2014). http://www.tandfonline.com/doi/abs/10.1080/15230406.2013.824637

  27. Šuba, R., Meijers, M., Oosterom, P.: Continuous road network generalization throughout all scales. ISPRS Int. J. Geo-Inf. 5(8), 145 (2016). http://www.mdpi.com/2220-9964/5/8/145

  28. Touya, G., Girres, J.F.: ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations. Cartogr. Geogr. Inf. Sci. 40(3), 192–200 (2013). http://www.tandfonline.com/doi/abs/10.1080/15230406.2013.809233

  29. Visvalingam, M., Whyatt, J.D.: Line generalisation by repeated elimination of points. Cartogr. J. 30(1), 46–51 (1993)

    Article  Google Scholar 

  30. Wang, L., Guo, Q., Liu, Y., Sun, Y., Wei, Z.: Contextual building selection based on a genetic algorithm in map generalization. ISPRS Int. J. Geo-Inf. 6(9), 271 (2017). http://www.mdpi.com/2220-9964/6/9/271

  31. Wu, X., Zhang, H., Xu, Y., Yang, J.: A comparative study of various properties to measure the road hierarchy in road networks. In: Zhou, C., Su, F., Harvey, F., Xu, J. (eds.) Spatial Data Handling in Big Data Era. AGIS, pp. 157–166. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-4424-3_11

    Chapter  Google Scholar 

  32. Yan, H., Weibel, R., Yang, B.: A multi-parameter approach to automated building grouping and generalization. GeoInformatica 12(1), 73–89 (2008). https://doi.org/10.1007/s10707-007-0020-5

    Article  Google Scholar 

  33. Zhou, Q., Li, Z.: A comparative study of various strategies to concatenate road segments into strokes for map generalization. Int. J. Geogr. Inf. Sci. 26(4), 691–715 (2012). http://www.tandfonline.com/doi/abs/10.1080/13658816.2011.609990

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Acknowledgment

Research were conducted within founds of Department of Mining Surveying and Environmental Engineering (AGH University of Science and Technology) no. 11.11.150.444. Research were conducted within founds of Faculty of Geology, Geophysics and Environmental Protection grant (AGH University of Science and Technology) no. 15.11.140.201.

Author information

Authors and Affiliations

  1. Department of Geoinformatics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Michał Lupa

  2. Department of Integrated Geodesy and Cartography, AGH University of Science and Technology, Krakow, Poland

    Stanisław Szombara & Krystian Kozioł

  3. Institute of Computer Science, Maria Curie Skłodowska University, Lublin, Poland

    Michał Chromiak

Authors
  1. Michał Lupa
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  2. Stanisław Szombara
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  3. Krystian Kozioł
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  4. Michał Chromiak
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Corresponding author

Correspondence to Michał Lupa .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Lupa, M., Szombara, S., Kozioł, K., Chromiak, M. (2018). The Use of Minimal Geometries in Automated Building Generalization. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety. BDAS 2018. Communications in Computer and Information Science, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-319-99987-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-99987-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99986-9

  • Online ISBN: 978-3-319-99987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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