1. A method comprising: receiving real time image data describing a real time image depicting a road scene; determining region of interest data describing a version of the real time image configured to remove redundant features from the real time image determining dense grid sampling data describing one or more features included in the version of the real time image described by the region of interest data; determining image feature vector data based on the one or more features included dense grid sampling data, the image feature vector data describing an image feature vector for the version of the real time image described by the region of interest data; and receiving client location data describing the geographic location of a client device associated with the road scene included in the real time image; determining offline road map data for the road scene based on the geographic location of the client device, the offline road map data describing (1) a static label for a road included in the road scene and (2) offline road information describing a regulatory speed limit for the road included in the road scene; and selecting, based on the static label, a classifier for analyzing the image feature vector; and executing, using a special-purpose processor device programmed to perform or control performance of the executing, the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene depicted in the real time image, wherein the real time driving difficulty category is determined based on the image feature vector, offline road information and runtime road description data describing the real time driving conditions of the road scene depicted in the real time image.