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Traffic Noise Estimation from Satellite Imagery with Deep Learning

@ IEEE Geoscience and Remote Sensing Symposium 2022



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

Leonardo Eicher, Michael Mommert, Damian Borth

14 Sep 2022

Item Type
Conference or Workshop Item
Journal Title
Subject Areas
computer science