Automated reconstruction of 3D input data for noise simulation

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.

A Harmonized Data Model for Noise Simulation in the EU

“…[17]. Reference [18] described an approach to automatically reconstruct input data for noise simulations in order to improve the efficiency and reliability, and consistency of different noise studies. For the quantitative analysis, the authors compared the differences between the simulation outcomes using the data created by their approach and the existing semi-automatic approach, for a study area in the Netherlands on about 1000 observation points.…”

Volume Comparison of Automatically Reconstructed Multi-Lod Building Models for Urban Planning Applications

“…The building geometries used in this paper were reconstructed using an improved version of the LoD1.3 reconstruction method described in Stoter et al (2020). This method creates building solids automatically from a set of building footprints and a classified aerial lidar point cloud.…”

“…The overall method is illustrated in Figure 2. The first three steps (a-c) are identical to the procedure described in Stoter et al (2020). First, the lidar points for each building are selected using a point-in-polygon procedure with the BAG footprints (Figure 2a).…”

“…They range from noise mapping, augmented reality, up to energy simulation tools (e.g. Stoter et al, 2020;Blut and Blankenbach, 2021;Wang et al, 2020;Rossknecht and Airaksinen, 2020). Biljecki et al (2015) provide a review of applications based on 3D city models.…”

Exploration of Open Data in Southeast Asia to Generate 3d Building Models

“…Much of the work done in the 3D GIS research community relies on the availability of open data. Besides the open data 3D city models released by local authorities (Schrotter and Hürzeler, 2020;Lehner and Dorffner, 2020), an increasing number of 3D city models are derived by conflating open datasets (Stoter et al, 2020;Kolbe et al, 2015). Such datasets originate from authorities and volunteered geoinformation, and have proven to be valuable in various spatial analyses (Saretta et al, 2020;Biljecki et al, 2016).…”