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Weather is an important factor affecting transportation and road safety. Inthis paper, we leverage state-of-the-art convolutional neural networks inlabelling images taken by street and highway cameras located across acrossNorth America. Road camera snapshots were used in experiments with multipledeep learning frameworks to classify images by road condition. The trainingdata for these experiments used images labelled as dry, wet, snow/ice, poor,and offline. The experiments tested different configurations of sixconvolutional neural networks (VGG-16, ResNet50, Xception, InceptionResNetV2,EfficientNet-B0 and EfficientNet-B4) to assess their suitability to thisproblem. The precision, accuracy, and recall were measured for each frameworkconfiguration. In addition, the training sets were varied both in overall sizeand by size of individual classes. The final training set included 47,000images labelled using the five aforementioned classes. The EfficientNet-B4framework was found to be most suitable to this problem, achieving validationaccuracy of 90.6%, although EfficientNet-B0 achieved an accuracy of 90.3% withhalf the execution time. It was observed that VGG-16 with transfer learningproved to be very useful for data acquisition and pseudo-labelling with limitedhardware resources, throughout this project. The EfficientNet-B4 framework wasthen placed into a real-time production environment, where images could beclassified in real-time on an ongoing basis. The classified images were thenused to construct a map showing real-time road conditions at various cameralocations across North America. The choice of these frameworks and our analysistake into account unique requirements of real-time map building functions. Adetailed analysis of the process of semi-automated dataset labelling usingthese frameworks is also presented in this paper.

Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks