Does a Finer Level of Detail of a 3D City Model Bring an Improvement for Estimating Shadows?

  • Filip BiljeckiEmail author
  • Hugo Ledoux
  • Jantien Stoter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


3D city models are characterised by the level of detail (LOD), which indicates their spatio-semantic complexity. Modelling data in finer LODs results in visually appealing models and opens the door for more applications, but that is at the expense of increased costs of acquisition, and larger storage footprint. In this paper we investigate whether the improvement in the LOD of a 3D building model brings more accurate shadow predictions. The result is that in most cases the improvement is negligible. Hence, the higher cost of acquiring 3D models in finer LODs is not always justified. However, the exact performance is influenced by the architecture of a building. The paper also describes challenges in experiments such as this one. For instance, defining error metrics may not always be simple, and the big picture of errors should be considered, as the impact of errors ultimately depends on the intended use case. For example, an error of a certain magnitude in estimating the shadow may not significantly affect visualisation purposes, but the same error may considerably influence the estimation of the photovoltaic potential.


Hausdorff Distance Error Metrics Relative Counterpart Area Error Flat Roof 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The detailed observations raised during the peer-review are gratefully acknowledged. We are thankful to the developers of the open-source software that was used in the realisation of this work, and to Ken Arroyo Ohori for suggestions which have accelerated the development of the method. This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs (project code: 11300).


  1. Agarwal, P. K., Har-Peled, S., Sharir, M., & Wang, Y. (2010). Hausdorff distance under translation for points and balls. ACM Transactions on Algorithms, 6(4), 1–26. Aug.CrossRefGoogle Scholar
  2. Alam, N., Coors, V., & Zlatanova, S. (2013). Detecting shadow for direct radiation using CityGML models for photovoltaic potentiality analysis. In C. Ellul, S. Zlatanova, M. Rumor, & R. Laurini (Eds.), Urban and regional data management (pp. 191–196). London, UK: CRC Press.CrossRefGoogle Scholar
  3. Appleton, K., & Lovett, A. (2003). GIS-based visualisation of rural landscapes: defining ‘sufficient’ realism for environmental decision-making. Landscape and Urban Planning, 65(3), 117–131.Google Scholar
  4. Arkin, E. M., Chew, L. P., Huttenlocher, D. P., Kedem, K., & Mitchell, J. S. B. (1991). An efficiently computable metric for comparing polygonal shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(3), 209–216. Mar.CrossRefGoogle Scholar
  5. Arroyo Ohori, K., Ledoux, H., Biljecki, F., & Stoter, J. (2015). Modeling a 3D city model and its levels of detail as a true 4D model. ISPRS International Journal of Geo-Information, 4(3), 1055–1075.Google Scholar
  6. Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (2002). MESH: measuring errors between surfaces using the Hausdorff distance. In IEEE international conference on multimedia and expo (ICME) (pp. 705–708). IEEE.Google Scholar
  7. Bartie, P., Reitsma, F., Kingham, S., & Mills, S. (2010). Advancing visibility modelling algorithms for urban environments. Computers, Environment and Urban Systems, 34(6), 518–531. Nov.CrossRefGoogle Scholar
  8. Besuievsky, G., Barroso, S., Beckers, B., & Patow, G. (2014). A configurable LoD for procedural urban models intended for daylight simulation. In G. Besuievsky & V. Tourre (Eds.), Proceedings of the eurographics workshop on urban data modelling and visualisation (pp. 19–24). Strasbourg, France: The Eurographics Association. Apr.Google Scholar
  9. Biljecki, F., & Arroyo Ohori, K. (2015). Automatic semantic-preserving conversion between OBJ and CityGML. In Eurographics workshop on urban data modelling and visualisation 2015 (pp. 25–30), Delft, Netherlands.Google Scholar
  10. Biljecki, F., Ledoux, H., & Stoter, J. (2014a). Error propagation in the computation of volumes in 3D city models with the Monte Carlo method. ISPRS annals photogrammetry, remote sensing and spatial information sciences, II(2), 31–39.Google Scholar
  11. Biljecki, F., Ledoux, H., Stoter, J., & Zhao, J. (2014b). Formalisation of the level of detail in 3D city modelling. Computers, Environment and Urban Systems, 48, 1–15. Nov.CrossRefGoogle Scholar
  12. Biljecki, F., Heuvelink, G. B. M., Ledoux, H., & Stoter, J. (2015a). Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs. International Journal of Geographical Information Science, 29(12), 2269–2294. Dec.Google Scholar
  13. Biljecki, F., Ledoux, H., & Stoter, J. (2015b). Improving the consistency of multi-LOD CityGML datasets by removing redundancy. In M. Breunig, A.-D. Mulhim, E. Butwilowski, P. V. Kuper, J. Benner, & K.-H. Häfele (Eds.), 3D geoinformation science (pp. 1–17). Springer.Google Scholar
  14. Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., & Çöltekin, A. (2015c). Applications of 3D city models: State of the art review. ISPRS International Journal of Geo-Information, 4(4), 2842–2889. Dec.
  15. Biljecki, F., Ledoux, H., & Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment and Urban Systems, 59, 25–37.CrossRefGoogle Scholar
  16. Blinn, J. (1988). Me and my (fake) shadow. IEEE Computer Graphics and Applications, 8(1), 82–86. Jan.Google Scholar
  17. Boeters, R., Arroyo Ohori, K., F., Biljecki, & S. Zlatanova. (2015). Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry. International Journal of Geographical Information Science, 29(12), 2248–2268.Google Scholar
  18. Booij, M. J. (2005). Impact of climate change on river flooding assessed with different spatial model resolutions. Journal of Hydrology, 303(1–4), 176–198. Mar.CrossRefGoogle Scholar
  19. Brasebin, M., Perret, J., Mustière, S., & Weber, C. (2012). Measuring the impact of 3D data geometric modeling on spatial analysis: illustration with Skyview factor. In T. Leduc, G. Moreau, & R. Billen (Eds.), Usage, usability, and utility of 3D city models—European COST action TU0801 (pp. (02001)1–16), Nantes, France, Oct. 2012. EDP Sciences.Google Scholar
  20. Bretagnon, P., & Francou, G. (1988). Planetary theories in rectangular and spherical variables. VSOP 87 solutions. Astronomy and Astrophysics, 202, 309–315. Aug.Google Scholar
  21. Burnicki, A. C., Brown, D. G., & Goovaerts, P. (2007). Simulating error propagation in land-cover change analysis: The implications of temporal dependence. Computers, Environment and Urban Systems, 31(3), 282–302. May.CrossRefGoogle Scholar
  22. Carneiro, C., & Golay, F. (2009). Solar radiation over the urban texture: LIDAR data and image processing techniques for environmental analysis at city scale. In J. Lee & S. Zlatanova (Eds.), 3D Geo-Information Sciences (pp. 319–340). Heidelberg: Springer.Google Scholar
  23. Chaubey, I., Cotter, A. S., Costello, T. A., & Soerens, T. S. (2005). Effect of DEM data resolution on SWAT output uncertainty. Hydrological Processes, 19(3), 621–628. Feb.CrossRefGoogle Scholar
  24. Cignoni, P., Rocchini, C., & Scopigno, R. (1998). Metro: Measuring error on simplified surfaces. Computer Graphics Forum, 17(2), 167–174. June.CrossRefGoogle Scholar
  25. City of Mississauga. Standards for shadow studies, Feb. 2012.
  26. Den Haag (2011). Voorstel van het college inzake beleid dakopbouwen. RIS, 180461.Google Scholar
  27. Eicker, U., Monien, D., Duminil, E., & Nouvel, R. (2015). Energy performance assessment in urban planning competitions. Applied Energy, 155, 323–333. Oct.CrossRefGoogle Scholar
  28. Girres, J.-F., & Touya, G. (2010). Quality assessment of the French openstreetmap dataset. Transactions in GIS, 14(4), 435–459.CrossRefGoogle Scholar
  29. Goodchild, M. F., & Hunter, G. J. (1997). A simple positional accuracy measure for linear features. International Journal of Geographical Information Science, 11(3), 299–306. Apr.CrossRefGoogle Scholar
  30. Gröger, G., & Plümer, L. (2012). CityGML—interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 12–33. July.CrossRefGoogle Scholar
  31. Hausdorff, F. (1914). Grundzüge der Mengenlehre. Leipzig, Germany: Verlag von Veit and Comp.Google Scholar
  32. Helbich, M., Jochem, A., Mücke, W., & Höfle, B. (2013). Boosting the predictive accuracy of urban hedonic house price models through airborne laser scanning. Computers, Environment and Urban Systems, 39(C), 81–92.Google Scholar
  33. Hengl, T. (2006). Finding the right pixel size. Computers and Geosciences, 32(9), 1283–1298. Nov.CrossRefGoogle Scholar
  34. Herbert, G., & Chen, X. (2015). A comparison of usefulness of 2D and 3D representations of urban planning. Cartography and Geographic Information Science, 42(1), 22–32.CrossRefGoogle Scholar
  35. Hobby, J. D. (1999). Practical segment intersection with finite precision output. Computational Geometry, 13(4), 199–214. Oct.CrossRefGoogle Scholar
  36. Hofierka, J., & Zlocha, M. (2012). A new 3-D solar radiation model for 3-D city models. Transactions in GIS, 16(5), 681–690. Oct.CrossRefGoogle Scholar
  37. Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850–863. Sept.CrossRefGoogle Scholar
  38. Hwang, R.-L., Lin, T.-P., & Matzarakis, A. (2011). Seasonal effects of urban street shading on long-term outdoor thermal comfort. Building and Environment, 46(4), 863–870. Apr.CrossRefGoogle Scholar
  39. Jochem, A., Höfle, B., Rutzinger, M., & Pfeifer, N. (2009). Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment. Sensors, 9(7), 5241–5262. July.CrossRefGoogle Scholar
  40. Kibria, M. S., Zlatanova, S., Itard, L., & Dorst, M. (2009). GeoVEs as tools to communicate in urban projects: requirements for functionality and visualization. In 3D geo-information sciences (pp. 379–395). Heidelberg: Springer.Google Scholar
  41. Knowles, R. L. (2003). The solar envelope: Its meaning for energy and buildings. Energy and Buildings, 35(1), 15–25. Jan.CrossRefGoogle Scholar
  42. Kumar, L., Skidmore, A. K., & Knowles, E. (1997). Modelling topographic variation in solar radiation in a GIS environment. International Journal of Geographical Information Science, 11(5), 475–497. July.CrossRefGoogle Scholar
  43. Lange, E., & Hehl-Lange, S. (2005). Combining a participatory planning approach with a virtual landscape model for the siting of wind turbines. Journal of Environmental Planning and Management, 48(6), 833–852. Nov.CrossRefGoogle Scholar
  44. Ledoux, H., Arroyo Ohori, K., & M. Meijers. (2014). A triangulation-based approach to automatically repair GIS polygons. Computers and Geosciences, 66, 121–131.Google Scholar
  45. Li, Y., Brimicombe, A. J., & Ralphs, M. P. (2000). Spatial data quality and sensitivity analysis in GIS and environmental modelling: the case of coastal oil spills. Computers, Environment and Urban Systems, 24(2), 95–108. Mar.CrossRefGoogle Scholar
  46. Ling, Y., Ehlers, M., Usery, E. L., & Madden, M. (2008). Effects of spatial resolution ratio in image fusion. International Journal of Remote Sensing, 29(7), 2157–2167. Apr.CrossRefGoogle Scholar
  47. Liu, L., Qiao, S., Zhang, Y., & Hu, J. (2012). An efficient outlying trajectories mining approach based on relative distance. International Journal of Geographical Information Science, 26(10), 1789–1810. Oct.CrossRefGoogle Scholar
  48. Luebke, D., Reddy, M., Cohen, J. D., Varshney, A., Watson, B., & Huebner, R. (2003). Level of detail for 3D graphics. Morgan Kaufmann Pub.Google Scholar
  49. Mardaljevic, J., & Rylatt, M. (2003). Irradiation mapping of complex urban environments: an image-based approach. Energy and Buildings, 35(1), 27–35. Jan.CrossRefGoogle Scholar
  50. Min, D., Zhilin, L., & Xiaoyong, C. (2007). Extended Hausdorff distance for spatial objects in GIS. International Journal of Geographical Information Science, 21(4), 459–475. Apr.CrossRefGoogle Scholar
  51. Möller, T., & Trumbore, B. (1997). Fast, minimum storage ray-triangle intersection. Journal of Graphics Tools, 2(1), 21–28.CrossRefGoogle Scholar
  52. Morello, E., & Ratti, C. (2009). Sunscapes: ‘Solar envelopes’ and the analysis of urban DEMs. Computers, Environment and Urban Systems, 33(1), 26–34.Google Scholar
  53. Mustiere, S., & Devogele, T. (2008). Matching networks with different levels of detail. GeoInformatica, 12(4), 435–453.CrossRefGoogle Scholar
  54. Nguyen, H. T., & Pearce, J. M. (2012). Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Solar Energy, 86(5), 1245–1260. May.CrossRefGoogle Scholar
  55. Pédrinis, F., Morel, M., & Gesquière, G. (2015). Change detection of cities. In 3D geoinformation science (pp. 123–139). Springer.Google Scholar
  56. Peters, R., Ledoux, H., & Biljecki, F. (2015). Visibility analysis in a point cloud based on the medial axis transform. In Eurographics workshop on urban data modelling and visualisation 2015 (pp. 7–12), Delft, Netherlands.Google Scholar
  57. Pogson, M., & Smith, P. (2015). Effect of spatial data resolution on uncertainty. Environmental Modelling and Software, 63, 87–96. Jan.CrossRefGoogle Scholar
  58. Redweik, P., Catita, C., Brito, M., & Brito, M. (2013). Solar energy potential on roofs and facades in an urban landscape. Solar Energy, 97, 332–341. Nov.CrossRefGoogle Scholar
  59. Ruiz, J. J., Ariza, F. J., Ureña, M. A., & Blázquez, E. B. (2011). Digital map conflation: a review of the process and a proposal for classification. International Journal of Geographical Information Science, 25(9), 1439–1466. Sept.CrossRefGoogle Scholar
  60. Samal, A., Seth, S., & Cueto, K. (2004). A feature-based approach to conflation of geospatial sources. International Journal of Geographical Information Science, 18(5), 459–489. July.CrossRefGoogle Scholar
  61. Stoter, J., de Kluijver, H., & Kurakula, V. (2008). 3D noise mapping in urban areas. International Journal of Geographical Information Science, 22(8), 907–924. Aug.CrossRefGoogle Scholar
  62. Strzalka, A., Bogdahn, J., Coors, V., & Eicker, U. (2011). 3D city modeling for urban scale heating energy demand forecasting. HVAC&R Research, 17(4), 526–539.Google Scholar
  63. Strzalka, A., Alam, N., Duminil, E., Coors, V., & Eicker, U. (2012). Large scale integration of photovoltaics in cities. Applied Energy, 93, 413–421. May.CrossRefGoogle Scholar
  64. Tooke, T. R., Coops, N. C., Voogt, J. A., & Meitner, M. J. (2011). Tree structure influences on rooftop-received solar radiation. Landscape and Urban Planning, 102(2), 73–81. Aug.CrossRefGoogle Scholar
  65. Usery, E. L., Finn, M. P., Scheidt, D. J., Ruhl, S., Beard, T., & Bearden, M. (2004). Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model. Journal of Geographical Systems, 6(3), 289–306. Oct.CrossRefGoogle Scholar
  66. Whitted, T. (1980). An improved illumination model for shaded display. Communications of the ACM, 23(6), 343–349. June.CrossRefGoogle Scholar
  67. Williams, L. (1978). Casting curved shadows on curved surfaces. ACM SIGGRAPH Computer Graphics, 12(3), 270–274. Aug.CrossRefGoogle Scholar
  68. Woo, A., Poulin, P., & Fournier, A. (1990). A survey of shadow algorithms. IEEE Computer Graphics and Applications, 10(6), 13–32.CrossRefGoogle Scholar
  69. Yezioro, A., & Shaviv, E. (1994). Shading: A design tool for analyzing mutual shading between buildings. Solar Energy, 52(1), 27–37. Jan.CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.3D GeoinformationDelft University of TechnologyDelftThe Netherlands

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