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On-line Access: 2021-07-20
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A review of computer graphics approaches to urban modeling from a machine learning perspective
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Tian Feng, Feiyi Fan, Tomasz Bednarz. A review of computer graphics approaches to urban modeling from a machine learning perspective[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 915-925.
@article{title="A review of computer graphics approaches to urban modeling from a machine learning perspective",
author="Tian Feng, Feiyi Fan, Tomasz Bednarz",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="7",
pages="915-925",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000141"
}
%0 Journal Article
%T A review of computer graphics approaches to urban modeling from a machine learning perspective
%A Tian Feng
%A Feiyi Fan
%A Tomasz Bednarz
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 7
%P 915-925
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000141
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A1 - Tian Feng
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A1 - Tomasz Bednarz
J0 - Frontiers of Information Technology & Electronic Engineering
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SP - 915
EP - 925
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000141
Abstract: urban modeling facilitates the generation of virtual environments for various scenarios about cities. It requires expertise and consideration, and therefore consumes massive time and computation resources. Nevertheless, related tasks sometimes result in dissatisfaction or even failure. These challenges have received significant attention from researchers in the area of computer graphics. Meanwhile, the burgeoning development of artificial intelligence motivates people to exploit machine learning, and hence improves the conventional solutions. In this paper, we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019. This serves as an overview of the current state of research on urban modeling from a machine learning perspective.
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