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A Review and Case Study of Neural Network Techniques for Automated Generation of High Level-of-Detail 3D City Models

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Gas Dynamics with Applications in Industry and Life Sciences (GKDLS 2021, GKDLS 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 429))

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Abstract

The growing interest in creating digital twins of cities has sparked a surge in the development of detailed 3D models. In this paper we examine the current state-of-the-art in generating high-resolution 3D models of cities using neural network techniques. Additionally, we showcase the outcomes of two case studies that demonstrate the practical applications of these techniques in 3D city model generation. The first case study focuses on rooftop segmentation using publicly available Swedish cadastral data, while the second case study explores façade feature extraction using Google Street View data.

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Acknowledgements

All authors gratefully acknowledge valuable input from Dag Wästberg and Sanjay Somanath for valuable assistance with this study. This work is part of the Digital Twin Cities Centre supported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-00041.

Author information

Authors and Affiliations

  1. Chalmers University of Technology, Sweden & Aristotle University of Thessaloniki, Thessaloniki, Greece

    Vasilis Naserentin

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Georgios Spaias, Anestis Kaimakamidis & Nikos Pitsianis

  3. Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Sweden, Greece

    Anders Logg

Authors
  1. Vasilis Naserentin
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  2. Georgios Spaias
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  4. Nikos Pitsianis
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  5. Anders Logg
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Corresponding author

Correspondence to Anders Logg .

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Editors and Affiliations

  1. Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, Gothenburg, Sweden

    Mohammad Asadzadeh

  2. Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, Gothenburg, Sweden

    Larisa Beilina

  3. Department of Aeronautics and Astronautics, Graduate School of Engineering, Kyoto University, Kyoto, Japan

    Shigeru Takata

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Naserentin, V., Spaias, G., Kaimakamidis, A., Pitsianis, N., Logg, A. (2023). A Review and Case Study of Neural Network Techniques for Automated Generation of High Level-of-Detail 3D City Models. In: Asadzadeh, M., Beilina, L., Takata, S. (eds) Gas Dynamics with Applications in Industry and Life Sciences. GKDLS GKDLS 2021 2022. Springer Proceedings in Mathematics & Statistics, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-031-35871-5_15

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