Traffic Noise Estimation from Satellite Imagery with Deep Learning
Road traffic noise represents a global health issue. Despite its importance, noise data are unavailable in many regions of the world. Such data are typically inferred through point measurements and complex physical models to simulate the propagation of noise. Since this process is unfeasible in many areas 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. ...