Traffic Noise Estimation from Satellite Imagery with Deep Learning

Item Type Conference or Workshop Item (Paper)
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
Authors Eicher, Leonardo; Mommert, Michael & Borth, Damian
Research Team AIML Lab
Language English
Subjects computer science
Date 20 July 2022
Publisher IEEE Geoscience and Remote Sensing Society
Page Range 5937-5940
Number of Pages 4
Event Title IEEE Geoscience and Remote Sensing Symposium 2022
Event Location Kuala Lumpur, Malaysia
Event Dates 17-22 July 2022
Official URL https://igarss2022.org/view_paper.php?PaperNum=318...
Contact Email Address michael.mommert@unisg.ch
Depositing User Prof. Dr. Michael Mommert
Date Deposited 14 Sep 2022 07:14
Last Modified 28 Sep 2022 09:19
URI: https://www.alexandria.unisg.ch/publications/267269

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Eicher, Leonardo; Mommert, Michael & Borth, Damian: Traffic Noise Estimation from Satellite Imagery with Deep Learning. 2022. - IEEE Geoscience and Remote Sensing Symposium 2022. - Kuala Lumpur, Malaysia.

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https://www.alexandria.unisg.ch/id/eprint/267269
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