Automated reconstruction of 3D input data for noise simulation.

Journal article (2020)

Authors

J.E. Stoter Urban Data Science - Architecture and the Built Environment
R.Y. Peters Urban Data Science - Architecture and the Built Environment
T.J.F. Commandeur Urban Data Science - Architecture and the Built Environment
B. Dukai Urban Data Science - Architecture and the Built Environment
Kavisha Kumar Urban Data Science - Architecture and the Built Environment
H. Ledoux Urban Data Science - Architecture and the Built Environment
Research Group
Urban Data Science (Architecture and the Built Environment) (TU Delft)
Copyright: © 2020 J.E. Stoter, R.Y. Peters, T.J.F. Commandeur, B. Dukai, Kavisha Kumar, H. Ledoux
DOI
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https://doi.org/10.1016/j.compenvurbsys.2019.101424
3D data reconstruction 3D urban application Noise simulation
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Published Date

2020

Language

English

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Faculty

Architecture and the Built Environment

Department

Urbanism

Research Group

Urban Data Science

Abstract

Noise is one of the main problems in urban areas. To monitor and manage noise problems, governmental organisations at all levels are obliged to regularly carry out noise studies. The simulation of noise is an important part of these studies. Currently, different organisations collect their own 3D input data as required in noise simulation in a semi-automated way, even if areas overlap. This is not efficient, but also differences in input data may lead to differences in the results of noise simulation which has a negative impact on the reliability of noise studies. To address this problem, this paper presents a methodology to automatically generate 3D input data as required in noise simulations (i.e. buildings, terrain, land coverage, bridges and noise barriers) from current 2D topographic data and point clouds. The generated data can directly be used in existing noise simulation software. A test with the generated data shows that the results of noise simulation obtained from our generated data are comparable to results obtained in a current noise study from practice. Automatically generated input data for noise simulation, as achieved in this paper, can be considered as a major step in noise studies. It does not only significantly improve the efficiency of noise studies, thus reducing their costs, but also assures consistency between different studies and therefore it improves the reliability and reproducibility. In addition, the availability of countrywide, standardised input data can help to advance noise simulation methods since the calculation method can be adopted to improved ways of 3D data acquisition and reconstruction.