Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Airborne synthetic aperture radar (SAR) has the potential to monitor remotely the road traffic infrastructure on a large scale. Of particular interest is the road surface roughness, which is an important road safety parameter. For this task, novel algorithms need to be developed. Machine learning approaches, such as artificial neural networks and random forest regression, which can perform nonlinear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with German Aerospace Center's (DLR)'s airborne F-SAR radar system. Several machine learning-based approaches were tested on the datasets to estimate road surface roughness. The resulting models were then compared with ground truth surface roughness values and also with the semiempirical surface roughness model studied in the previous work.