Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Influence of land cover on noise simulation output – A case study in Malmö, Sweden

Pantazatou, Karolina LU orcid ; Mattisson, Kristoffer LU orcid ; Olsson, Per-Ola LU ; Telldén, Erik ; Kettisen, Anders ; Hosseinvash Azari, Soraya ; Liu, Wenjing and Harrie, Lars LU orcid (2025) In Noise Mapping 12(1).
Abstract
Determining the land cover (LC) data requirements used as input to noise simulations is essential for planning sustainable urban densifications. This study examines how different LC datasets influence simulated environmental noise levels of road traffic using Nord2000 in an urban area of 1 km2 in southern Sweden. Four LC datasets were used. The first dataset was based on satellite data (spatial resolution 10 m) combined with various other datasets implementing an LC classification algorithm prioritizing vegetation. The second dataset was created by applying an LC majority priority rule over every cell of the first dataset. The third dataset was produced by applying a convolutional neural network over an orthophoto (0.08 m spatial... (More)
Determining the land cover (LC) data requirements used as input to noise simulations is essential for planning sustainable urban densifications. This study examines how different LC datasets influence simulated environmental noise levels of road traffic using Nord2000 in an urban area of 1 km2 in southern Sweden. Four LC datasets were used. The first dataset was based on satellite data (spatial resolution 10 m) combined with various other datasets implementing an LC classification algorithm prioritizing vegetation. The second dataset was created by applying an LC majority priority rule over every cell of the first dataset. The third dataset was produced by applying a convolutional neural network over an orthophoto (0.08 m spatial resolution), while the fourth dataset was created by manually digitizing ground surfaces over the same orthophoto also utilizing data from the municipality’s basemap. The results show that LC data impact simulated noise levels, with priority rules in LC classification algorithms having a greater effect than spatial resolution. Statistically significant differences (up to 3 dB(A)) were found when comparing the simulated noise levels generated using the vegetation-prioritizing LC dataset compared to the simulated noise levels of the other LC datasets. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Noise Simulations, Nord2000, Convolutional Neural Networks (CNN), Land Cover, Semantic 3D city models, sustainable urban planning
in
Noise Mapping
volume
12
issue
1
publisher
De Gruyter
external identifiers
  • scopus:105003189810
DOI
10.1515/noise-2025-0016
language
English
LU publication?
yes
id
27da65c0-3791-4f53-be95-fb024d92082d
date added to LUP
2025-04-22 14:39:25
date last changed
2025-05-05 04:01:00
@article{27da65c0-3791-4f53-be95-fb024d92082d,
  abstract     = {{Determining the land cover (LC) data requirements used as input to noise simulations is essential for planning sustainable urban densifications. This study examines how different LC datasets influence simulated environmental noise levels of road traffic using Nord2000 in an urban area of 1 km2 in southern Sweden. Four LC datasets were used. The first dataset was based on satellite data (spatial resolution 10 m) combined with various other datasets implementing an LC classification algorithm prioritizing vegetation. The second dataset was created by applying an LC majority priority rule over every cell of the first dataset. The third dataset was produced by applying a convolutional neural network over an orthophoto (0.08 m spatial resolution), while the fourth dataset was created by manually digitizing ground surfaces over the same orthophoto also utilizing data from the municipality’s basemap. The results show that LC data impact simulated noise levels, with priority rules in LC classification algorithms having a greater effect than spatial resolution. Statistically significant differences (up to 3 dB(A)) were found when comparing the simulated noise levels generated using the vegetation-prioritizing LC dataset compared to the simulated noise levels of the other LC datasets.}},
  author       = {{Pantazatou, Karolina and Mattisson, Kristoffer and Olsson, Per-Ola and Telldén, Erik and Kettisen, Anders and Hosseinvash Azari, Soraya and Liu, Wenjing and Harrie, Lars}},
  keywords     = {{Noise Simulations; Nord2000; Convolutional Neural Networks (CNN); Land Cover; Semantic 3D city models; sustainable urban planning}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{1}},
  publisher    = {{De Gruyter}},
  series       = {{Noise Mapping}},
  title        = {{Influence of land cover on noise simulation output – A case study in Malmö, Sweden}},
  url          = {{http://dx.doi.org/10.1515/noise-2025-0016}},
  doi          = {{10.1515/noise-2025-0016}},
  volume       = {{12}},
  year         = {{2025}},
}