Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 119-126, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W10-119-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018

TESTING THE IMPACT OF 2D GENERALISATION ON 3D MODELS – EXPLORING ANALYSIS OPTIONS WITH AN OFF-THE-SHELF SOFTWARE PACKAGE

E. Muñumer Herrero1, C. Ellul1, and J. Morley2 E. Muñumer Herrero et al.
  • 1Department of Civil, Environmental and Geomatic Engineering, University College London, UK
  • 2Ordnance Survey, Southampton, UK

Keywords: 3D generalisation, 3D models, 3D buildings, simplification, aggregation, performance

Abstract. Popularity and diverse use of 3D city models has increased exponentially in the past few years, providing a more realistic impression and understanding of cities. Often, 3D city models are created by elevating the buildings from a detailed 2D topographic base map and subsequently used in studies such as solar panel allocation, infrastructure remodelling, antenna installations or even tourist guide applications. However, the large amount of resulting data slows down rendering and visualisation of the 3D models, and can also impact the performance of any analysis. Generalisation enables a reduction in the amount of data – however the addition of the third dimension makes this process more complex, and the loss of detail resulting from the process will inevitably have an impact on the result of any subsequent analysis.

While a few 3D generalization algorithms do exist in a research context, these are not available commercially. However, GIS users can create the generalised 3D models by simplifying and aggregating the 2D dataset first and then extruding it to the third dimension. This approach offers a rapid generalization process to create a dataset to underpin the impact of using generalised data for analysis. Specifically, in this study, the line of sight from a tall building and the sun shadow that it creates are calculated and compared, in both original and generalised datasets. The results obtained after the generalisation process are significant: both the number of polygons and the number of nodes are minimized by around 83% and the volume of 3D buildings is reduced by 14.87%. As expected, the spatial analyses processing times are also reduced. The study demonstrates the impact of generalisation on analytical results – which is particularly relevant in situations where detailed data is not available and will help to guide the development of future 3D generalisation algorithms. It also highlights some issues with the overall maturity of 3D analysis tools, which could be one factor limiting uptake of 3D GIS.

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