Skip to main content
SpringerLink
Log in

Simplification of 3D City Models Based on K-Means Clustering

  • Conference paper
  • First Online:
Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

Included in the following conference series:

  • 1161 Accesses

Abstract

With the development of smart cities, 3D city models have expanded from simple visualization to more applications. However, the data volume of 3D city models is also increasing at the same time, which brings great pressure to data storage and visualization. Therefore, it is necessary to simplify 3D models. In this paper, a three-step simplification method is proposed. Firstly, the geometric features of the building are used to extract the walls and roof of the building separately, and then the ground plan and the single-layer roof are extracted by the K-Means clustering algorithm. Finally, the ground plan is raised to intersect with the roof polygon to form a simplified three-dimensional city model. In this paper, experiments are carried out on a certain number of 3D city models of CityGML format. The compression ratio of model data is 92.08%, the simplification result shows better than others.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lan, R., Wang, J.: Research on support evaluation system of spatial information infrastructure in smart cities. J. Surv. Mapp. Sci. Technol. (1), 78–81 (2015)

    Google Scholar 

  2. Kada, M.: 3D Building generalization based on half-space modeling. In: Proceedings of the ISPRS Workshop on Multiple Representation and Interoperability of Spatial Data (2006)

    Google Scholar 

  3. Baig, S.U., Rahman, A.A.: A three-step strategy for generalization of 3D building models based on CityGML specifications. GeoJournal 78(6), 1013–1020 (2013)

    Article  Google Scholar 

  4. Li, Q., Sun, X., Yang, B., et al.: Geometric structure simplification of 3D building models[J]. ISPRS J. Photogram. Remote Sens. 84, 100–113 (2013)

    Article  Google Scholar 

  5. Ying, S., Guo, R., Li, L., et al.: Construction of 3D volumetric objects for a 3D cadastral system. Trans. GIS 19(5), 758–779 (2015)

    Article  Google Scholar 

  6. Fan, H., Meng, L., Jahnke, M.: Generalization of 3D Buildings Modelled by CityGML. In: Advances in GIScience, Proceedings of the Agile Conference, Hannover, Germany, 2–5 June. DBLP, pp. 387-405 (2009)

    Google Scholar 

  7. Fan, H., Meng, L.: A three-step approach of simplifying 3D buildings modeled by CityGML. Int. J. Geogr. Inf. Sci. 26(6), 1091–1107 (2012)

    Article  Google Scholar 

  8. Mao, B., Ban, Y., Harrie, L.: A multiple representation data structure for dynamic visualisation of generalised 3D city models[J]. ISPRS J. Photogram Remote Sens. 66(2), 198–208 (2011)

    Article  Google Scholar 

  9. Biljecki, F., Ledoux, H., Stoter, J., et al.: Formalisation of the level of detail in 3D city modelling. Comput. Environ. Urban Syst. 48(16), 1–15 (2014)

    Article  Google Scholar 

  10. Geiger, A., Benner, J., Haefele, K.: Generalization of 3D IFC building models. In: Breunig, M., Al-Doori, M., Butwilowsk, E., Kuper, P., Benner, J., Haefele, K. (eds.) 3D Geoinformation Science., pp. 19–35. Springer, Cham (2015)

    Chapter  Google Scholar 

  11. Mortara, M., Patane, G., Spagnuolo, M., Falcidieno, B., Rossignac, J.: Plumber: a method for a multi-scale decomposition of 3D shapes into tubular primitives and bodies. In: Proceedings of ACM Symposium on Solid Modeling and Applications, pp. 139–158 (2009)

    Google Scholar 

  12. Buchanan, K., Gaytan, D., Xu, L., Dilay, C., Hilton, D.: Spatial K-means clustering of HF noise trends in Southern California waters. 2018 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, pp. 1–2 (2018)

    Google Scholar 

  13. Shao, Lun, Zhou, Xinzhi, et al.: Improved K-means clustering algorithm based on multi-dimensional grid space. Comput. Appl. 38(10), 104–109 (2018)

    Google Scholar 

  14. Zhang, C., Mao, B.: 3D Building models segmentation based on K-means++ cluster analysis. In:. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, vol. XLII-2/W2, pp. 57–61 (2016)

    Google Scholar 

  15. Graham, R.L.: An efficient algorithm for determining the convex hull of a planar set. Inf. Process.Lett. 1, 132–133 (1972)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by National Natural Science Foundation of China (41671457), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA170003).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Modern Logistics, Jiangsu Key Laboratory of Grain Big Data Mining and Application, College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, Jiangsu, China

    Hui Cheng & Bo Mao

  2. College of Electrical Engineering and Automation, Jiangsu Maritime Institute, No. 309 Gezhi Road, Nanjing, Jiangsu, China

    Bingchan Li

Authors
  1. Hui Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingchan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Mao .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, H., Li, B., Mao, B. (2020). Simplification of 3D City Models Based on K-Means Clustering. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2810-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation