A Review and Case Study of Neural Network Techniques for Automated Generation of High Level-of-Detail 3D City Models
Paper in proceeding, 2023

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 fa├žade feature extraction using Google Street View data.

Digital twins of cities

3D city models

Neural networks


Vasilis Naserentin

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Georgios Spaias

Aristotle University of Thessaloniki

Anestis Kaimakamidis

Aristotle University of Thessaloniki

Nikos Pitsianis

Aristotle University of Thessaloniki

Anders Logg

University of Gothenburg

Springer Proceedings in Mathematics and Statistics

21941009 (ISSN) 21941017 (eISSN)

Vol. 429 261-283
9783031358708 (ISBN)

Annual workshops for Swedish Alumni Club of Japan Society for the Promotion of Science, JSPS/SAC 2021 and 2022
Virtual, Online, ,

Digital Twin Cities Centre

VINNOVA (2019-00041), 2020-02-29 -- 2024-12-31.

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Information Science



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