| 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 |
Traffic Noise Estimation from Satellite Imagery with Deep Learning
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