Automatic Semantic and Geometric Enrichment of CityGML Building Models Using HOG-Based Template Matching

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Semantically rich 3D building models give the potential for a wealth of rich geo-spatially-enabled applications such as cultural heritage augmented reality, urban planning, radio network planning and personal navigation. However, the majority of existing building models lack much if any semantic detail. This work demonstrates a novel method for automatically locating subclasses of windows and doors, using computer vision techniques including the histogram of oriented gradient (HOG) template matching, and automatically creating enriched CityGML content for the matched windows and doors. Good results were achieved for class identification with potential for further refinement of subclasses of windows and doors and other architectural features. It is part of a wider project to bring even richer semantic content to 3D geo-spatial building models.


Semantic Geometric CityGML HOG Template matching 



Funded by an EPSRC Industrial CASE studentship with Ordnance Survey, GB; special thanks go to Isabel Sargent and David Holland from Ordnance Survey. Aside from templates 13 and 14 (see Fig. 4) all data used in this work are already publicly available at the locations referenced in the text.


  1. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Paper presented at the 17th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, USA 20–26 June 2005.Google Scholar
  2. Dawe, S. (2013). King of the castles: Britain’s built heritage rules Huffington post. Retrieved July 11, 2014.
  3. de Fornel, P., & Sizun, H. (2006). Radio wave propagation for telecommunication applications. Berlin: Springer.Google Scholar
  4. Debevec, P. E., Taylor, C. J., & Malik, J. (1996). Modeling and rendering architecture from photographs: A hybrid geometry-and-image-based approach. Paper presented at the 23rd International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), New Orleans, USA, 04–09 August 1996.Google Scholar
  5. Dick, A. R., Torr, P. H. S., & Cipolla, R. (2004). Modelling and interpretation of architecture from several images. International Journal of Computer Vision, 60(2), 111–134.CrossRefGoogle Scholar
  6. Döllner, J., & Hagedorn, B. (2007). Integrating urban GIS, CAD, and BIM data by service based virtual 3D city models. Urban and regional data management—annual. Leiden: Taylor & Francis.Google Scholar
  7. Dore, C., & Murphy, M. (2014). Semi-automatic techniques for generating BIM façade models of historic buildings. Journal of Information Technology in Construction, 19(2), 20–46.Google Scholar
  8. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(9), 1627–1645.CrossRefGoogle Scholar
  9. Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 726–740.CrossRefGoogle Scholar
  10. Gröger, G., Kolbe, T., Nagel, C., & Häfele, K. (2012). OGC city geography markup language (CityGML) en-coding standard. Open Geospatial Consortium.Google Scholar
  11. Gröger, G., & Plümer, L. (2012). CityGML–interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, 71(July), 12–33.CrossRefGoogle Scholar
  12. Ham, Y., & Golparvar-Fard, M. (2015). Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling. Automation in Construction, 49(Part B), 214–224.Google Scholar
  13. Hampson, K., Kraatz, J. A., & Sanchez, A. X. (2014). The global construction industry and R&D. R&D Investment and Impact in the Global Construction Industry. Abingdon: Taylor & Francis.Google Scholar
  14. Iqbal, Q., & Aggarwal, J. K. (2002). Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recognition, 35(7), 1463–1479.CrossRefGoogle Scholar
  15. Isikdag, U., & Zlatanova, S. (2009). Towards defining a framework for automatic generation of buildings in CityGML using building information models. Lecture Notes in Geoinformation and Cartography—3D Geo-Information Sciences. Berlin: Springer.Google Scholar
  16. Johansson, B., & Kahl, F. (2002). Detecting windows in city scenes. Lecture Notes in Computer Science—Pattern Recognition with Support Vector Machines. Berlin: Springer.Google Scholar
  17. Jones, C. B., Rosin, P. L., & Slade, J. (2014). Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations. Paper presented at the 25th International Conference on Computational Linguistics (COLING)—3rd Workshop on Vision and Language (VL), Dublin, Ireland, 23 August 2014.Google Scholar
  18. Kolbe, T. H. (2009). Representing and Exchanging 3D City Models with CityGML. Lecture Notes in Geoinformation and Cartography—3D Geo-Information Sciences. Berlin: Springer.Google Scholar
  19. Koutamanis, A., & Mitossi, V. (1993). Computer vision in architectural design. Design Studies, 14(1), 40–57.CrossRefGoogle Scholar
  20. Koziński, M., & Marlet, R. (2014). Image parsing with graph grammars and Markov Random Fields applied to facade analysis. Paper presented at the 14th IEEE Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, USA, 24–26 March 2014.Google Scholar
  21. Kroon, D.-J. (2011). Fast/robust template matching. MathWorks Inc. Retrieved September 01, 2014.
  22. Mayer, H., & Reznik, S. (2005). Building facade interpretation from image sequences. Paper presented at the ISPRS Workshop on Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring—Concepts, Algorithms, and Evaluation (CMRT)—WG III/4–5 IV/3, Vienna, Austria, 29–30 August 2005.Google Scholar
  23. Meixner, P., Leberl, F., & Brédif, M. (2011). Interpretation of 2D and 3D building details on facades and roofs. Paper presented at the 3rd Conference on Photogrammetric Image Analysis (PIA)—ISPRS Technical Commission III Symposium, München, Germany, 5–7 October 2011.Google Scholar
  24. Ok, D., Kozinski, M., Marlet, R., & Paragios, N. (2012). High-level bottom-up cues for top-down parsing of facade images. Paper presented at the 2nd Joint 3DIM/3DPVT International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), Zürich, Switzerland, 13–15 October 2012.Google Scholar
  25. Pevsner, N., Harris, J., & Antram, N. (1989). Lincolnshire. London: Yale University Press.Google Scholar
  26. Reznik, S., & Mayer, H. (2007). Implicit shape models, model selection, and plane sweeping for 3D facade interpretation. Paper presented at the 2nd Conference on Photogrammetric Image Analysis (PIA)—ISPRS Technical Commission III Symposium, München, Germany, 19–21 September 2007.Google Scholar
  27. Ross, L., Bolling, J., Döllner, J., & Kleinschmit, B. (2009). Enhancing 3D city models with heterogeneous spatial information: Towards 3D land information systems. Lecture Notes in Geoinformation and Cartography—Advances in GIScience—12th AGILE Conference. Berlin: Springer.Google Scholar
  28. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1–47.CrossRefGoogle Scholar
  29. Sivic, J., & Efros, A. A. (2014). Urban-scale quantitative visual analysis. ERCIM News—Special Theme: Smart Cities, 98, 43–44.Google Scholar
  30. Smart, P. D., Quinn, J. A., & Jones, C. B. (2011). City model enrichment. ISPRS Journal of Photogrammetry and Remote Sensing, 66(2), 223–234.CrossRefGoogle Scholar
  31. Sonka, M., Hlaváč, V., & Boyle, R. (2014). Image processing, analysis, and machine vision (4th ed.). Boston: Cengage Learning.Google Scholar
  32. Stadler, A., & Kolbe, T. H. (2007). Spatio-semantic coherence in the integration of 3D city models. Paper presented at the 5th International Symposium on Spatial Data Quality (ISSDQ), Enschede, The Netherlands, 13–15 June 2007.Google Scholar
  33. van den Brink, L., Stoter, J., & Zlatanova, S. (2013). Establishing a national standard for 3D topographic data compliant to CityGML. International Journal of Geographical Information Science, 27(1), 92–113.CrossRefGoogle Scholar
  34. Whiteside, A. (2009). Definition identifier URNs in OGC namespace. OpenGIS Best Practice document.Google Scholar
  35. Xiao, J. (2013). HOG-based template matching. Retrieved January 10, 2015.
  36. Zhang, Y., Xiao, J., Hays, J., & Tan, P. (2013). FrameBreak: Dramatic image extrapolation by guided shift-maps. Paper presented at the 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, 23–28 June 2013.Google Scholar
  37. Zhu, Q., Hu, M., Zhang, Y., & Du, Z. (2009). Research and practice in three-dimensional city modeling. Geo-spatial Information Science, 12(1), 18–24.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science & InformaticsCardiff UniversityCardiffUK

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