Rainfall Estimation from Traffic Cameras

We propose and evaluate a method for the estimation of rainfall from images from a network of traffic cameras and rain gauges. The method trains a neural network for each camera under the supervision of the rain gauges and interpolates the results to estimate rainfall at any location. We study and evaluate variants of the method that exploit feature extraction and various interpolation methods. We empirically and comparatively demonstrate the superiority of a hybrid approach and of the inverse distance weighting interpolation for an existing comprehensive network of publicly accessible weather stations and traffic cameras.