Quantifying Traffic with Deep Learning from Earth Observation Data

Transportation by means of internal combustion still constitutes the main mode of mobility. As such, it exerts adverse effects on human health and Earth's climate through the emission of exhaust gases and noise. We present two works that aim to quantify commercial vehicle traffic [1] and to estimate road traffic noise [2]. In both works, we rely on Deep Learning methods and freely available multi-band satellite imagery from Sentinel-2. We utilize ground-truth traffic rates and road noise estimates available for Switzerland to train and evaluate our approaches. We find that we can quantify commercial vehicle traffic rates with an RMSE down to 60 vehicles per hour and we can estimate road traffic noise levels with an RMSE down to 9 dBA for any location in Switzerland. Our studies show that satellite-based observations in combination with Deep Learning are sufficient to estimate commercial vehicle traffic rates and resulting road traffic noise and that this method may provide reasonable approximations in places where no ground-truth data is available.

[1]: Blattner, Moritz; Mommert, Michael & Borth, Damian: Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning. 2021. - ICML 2021 Workshop on Tackling Climate Change with Machine Learning Workshop.
[2]: 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.