A Harmonized Data Model for Noise Simulation in the EU

Kumar, Kavisha; Ledoux, Hugo; Schmidt, Richard; Verheij, Theo; Stoter, J.E.

doi:10.3390/ijgi9020121

Cited by 12 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final goal of this 3D data project, is to support applications with standardised and future-proof 3D topographic data. At this moment the 3D data can directly be used in 3D noise simulation software as prescribed by the Dutch government (Stoter et al, 2020;Kumar et al, 2020).…”

Section: Use Of the 3d Data In Applicationsmentioning

confidence: 99%

Generating, Storing, Updating and Disseminating a Countrywide 3d Model

Dukai

Peters

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. As in many countries, in The Netherlands governmental organisations are acquiring 3D city models to support their public tasks. However, this is still being done within individual organisation, resulting in differences in 3D city models within one country and sometimes covering the same area: i.e. differences in data structure, height references used, update cycle, data quality, use of the 3D data etc. In addition, often only large governmental organisations can afford investing in 3D city models (and the required knowledge) and not small organisations, like small municipalities. To address this problem, the Dutch Kadaster is collaborating with the 3D Geoinformation research group at TU Delft to generate and disseminate a 3D city model covering the whole of the Netherlands and to do this in a sustainable manner, i.e. with an implementation that ensures periodical updates and that aligns with the 3D city models of other governmental organisations, such as large cities. This article describes the workflow that has been developed and implemented.

show abstract

Section: Use Of the 3d Data In Applicationsmentioning

confidence: 99%

Generating, Storing, Updating and Disseminating a Countrywide 3d Model

Dukai

Peters

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The quality of input data is a key issue for preparing strategic noise maps with sufficient accuracy. As each EU member state can use different sources for input levels, it is difficult to obtain comparable results in spite of the common calculation method [11]. A data guideline would be needed to solve that problem [11], but no instructions have been published so far.…”

Section: Introductionmentioning

confidence: 99%

“…As each EU member state can use different sources for input levels, it is difficult to obtain comparable results in spite of the common calculation method [11]. A data guideline would be needed to solve that problem [11], but no instructions have been published so far. Therefore, each member state is responsible for the selection of input data sources.…”

Section: Introductionmentioning

confidence: 99%

“…Several data sources for buildings used in sound propagation model calculations are discussed in the relevant literature, some of which provide data at the international level, while others are only available in a given country. The data model for noise simulation studied by Kumar et al contains the following data for buildings: class of building, function (residential, public or industrial), usage, year of construction, year of demolition, type of the roof, measured height, number of the stories above and below ground and their heights [11]. Morley et al examined two versions concerning the building height: in the higher resolution version, individual building heights from LiDAR measurements were applied, while in the lower resolution case generalized surface provided by averaging the LiDAR building heights over a 50 m grid or the constant value of 9.5 m were used [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity of CNOSSOS-EU sound propagation model to digital surface components

Balogh

Schmelz

Orosz

2022

Acta Tech. Jaurinensis

View full text Add to dashboard Cite

The accuracy of input data is a key issue in sound propagation model calculations. This paper aimed to assess the effect of building and land cover input data accuracy on CNOSSOS-EU sound propagation model outputs. Calculations were performed for a study site with a quite diverse land cover structure, located along a major road in Monor, Hungary. Nine test cases were defined based on building and land cover datasets with different accuracy. Comparing the results of the test cases to each other, it was found that in residential areas, the sound propagation model is more sensitive to the building data than to the land cover data. Therefore, it is recommended to use more detailed building input data in those areas, while using a land cover database with higher resolution than CORINE land cover data does not provide significantly better results. In non-residential areas, the influence of land cover input data on model results increases significantly.

show abstract

“…Although the CNOSSOS model is more advanced than most of the empirical models, it is not so easy to implement. A software implementation is needed, and practical guidelines for the input data design to test the real-world situation are limited [15]. A practical implementation of the CNOSSOS model was performed to predict urban noise in [16].…”

Section: Introductionmentioning

confidence: 99%

Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network

2021

View full text Add to dashboard Cite

With the drastically increasing traffic in the last decades, crucial environmental problems have been caused, such as greenhouse gas emission and traffic noise pollution. These problems have adversely affected our life quality and health conditions. In this paper, modelling of traffic noise employing deep learning is investigated. The goal is to identify the best machine-learning model for predicting traffic noise from real-life traffic data with multivariate traffic features as input. An extensive study on recurrent neural network (RNN) is performed in this work for modelling time series traffic data, which was collected through an experimental campaign at an inner city roundabout, including both video traffic data and audio data. The preprocessing of the data, namely how to generate the appropriate input and output for deep learning model, is detailed in this paper. A selection of different architectures of RNN, such as many-to-one, many-to-many, encoder–decoder architectures, was investigated. Moreover, gated recurrent unit (GRU) and long short-term memory (LSTM) were further discussed. The results revealed that a multivariate bi-directional GRU model with many-to-many architecture achieved the best performance with both high accuracy and computation efficiency. The trained model could be promising for a future smart city concept; with the proposed model, real-time traffic noise predictions can be potentially feasible using only traffic data collected by different sensors in the city, thanks to the generated big data by smart cities. The forecast of excessive noise exposure can help the regulation and policy makers to make early decisions, in order to mitigate the noise level.

show abstract

A Harmonized Data Model for Noise Simulation in the EU

Cited by 12 publications

References 14 publications

Generating, Storing, Updating and Disseminating a Countrywide 3d Model

Generating, Storing, Updating and Disseminating a Countrywide 3d Model

Sensitivity of CNOSSOS-EU sound propagation model to digital surface components

Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network

Contact Info

Product

Resources

About