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Reconstructing 3D Building Models with the 2D Cadastre for Semantic Enhancement

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

Abstract

Virtual city models are increasingly used in urban land management processes, which involve the use of different sources of spatial information. This heterogeneous data is, however, often complementary and it may be necessary to give the possibility to join information provided by different sources. This paper presents a method to enhance 3D buildings by using usual 2D vectorial polygon database. These polygons may represent districts, building footprints, or any segmentation of the urban area that adds information to the city model. The enhancement consists in using this polygon database to split the 3D buildings into a set of city objects where each element possesses a 3D geometry and the semantic information of the polygon it is linked to. In this paper, for an illustration purpose, we will present how to create this link between 3D buildings and the cadastre map, in order to create a set of semantically rich 3D building models.

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Notes

  1. 1.

    http://www.citygml.org/.

  2. 2.

    http://www.opengeospatial.org/.

  3. 3.

    http://data.grandlyon.com/.

  4. 4.

    http://liris.cnrs.fr/vcity/wiki/doku.php.

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Acknowledgments

This work was performed within the BQI program of Université Lyon 1. This work was also supported by the LABEX IMU (ANR-10-LABX-0088) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). Data are provided by “Lyon Métropole”. The authors would like to thanks the DINSI team of Lyon Métropole for their valued feedbacks during this collaboration.

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Correspondence to Frédéric Pedrinis .

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Pedrinis, F., Gesquière, G. (2017). Reconstructing 3D Building Models with the 2D Cadastre for Semantic Enhancement. In: Abdul-Rahman, A. (eds) Advances in 3D Geoinformation. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-25691-7_7

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