Road scene classification based on street-level images and spatial data

Understanding the context of the scene is one of the most important aspects for new generation of autonomous vehicles. It is a very trivial task for a human to recognize the scene context by only a single look at the picture, however, for a computer, it is still a challenging task. This problem can be solved by automatic data labeling using deep-learning models for scene classification. Relying on scene type labels we can select relevant scenes to prepare a balanced dataset to train more advanced instance detection models using data from a specific road condition.This study presents a novel framework based on a deep convolutional neural network (CNN) for the automatic road scene classification of street-level automotive images. For the evaluation of our approach, we use a well-known autonomous benchmark dataset, from which we extract geo-position data and combine them with predictions from the scene classification model to get ground truth labels to train and evaluate a ResNet-50 model for scene classification. The results and comparison with state-of-the-art methods are presented.