Traffic Noise Estimation from Satellite Imagery with Deep Learning
Type
conference paper
Date Issued
2022-07-20
Author(s)
Research Team
AIML Lab
Abstract
Road traffic noise represents a global health issue. Despite its importance, noise data are unavailable in many regions of the world. We therefore propose to approximate noise data from satellite imagery in an end-to-end Deep Learning approach. We train a U-Net segmentation model to estimate road noise based on freely available Sentinel-2 satellite imagery and existing road traffic noise estimates for Switzerland. We are able to achieve an RMSE of 8.8 dB(A) for day-time traffic noise and 7.6 dB(A) for nighttime traffic noise with a spatial resolution of 10 m. In addition to identifying major road networks, our model succeeds to predict the spatial propagation of noise. Our results suggest that this approach provides a pathway to estimating road traffic noise for areas for which no such measures are available
Language
English
Publisher
IEEE Geoscience and Remote Sensing Society
Start page
5937
End page
5940
Pages
4
Event Title
IEEE Geoscience and Remote Sensing Symposium 2022
Event Location
Kuala Lumpur, Malaysia
Event Date
17-22 July 2022
Subject(s)
Division(s)
Contact Email Address
michael.mommert@unisg.ch
Eprints ID
267269
File(s)![Thumbnail Image]()
Name
IGARSS_traffic_noise.pdf
Size
3.13 MB
Format
Adobe PDF
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