Given the scarcity of internationally recognized standardized RSCs (road surface conditions) datasets, particularly those documenting RSCs under extreme weather events, this study presents a comprehensive dataset on road conditions during snow and ice disasters. The dataset fills a critical gap in the field and provides valuable resources to enhance the performance and accuracy of RSCs recognition models.

  Focusing specifically on RSCs under snow and ice disasters, the dataset is structured based on statistical analyses of the impact of extreme weather on traffic control, categorizing RSCs into three main types: icy roads, blowing snow, and heavy snowfall. Data sources include highway cameras, mobile devices, and online resources, resulting in a dataset that encompasses six typical RSCs: dry, snowy, icy, snow-blown, melting snow, and slippery roads.

  In the data processing phase, to prevent potential correlations introduced by data augmentation that could affect the accuracy and reliability of model performance evaluation, a cautious approach was adopted. Initially, the raw dataset was divided into training, validation, and test sets, ensuring direct independence between these subsets. Subsequently, data augmentation operations, such as flipping, rotating, translating, and adding Gaussian noise, were applied separately to each subset to minimize data crossover effects caused by the sequence of augmentation steps. Following multiple augmentation strategies, the dataset was expanded to a total of 9,000 images.

  To further improve the training efficiency and convergence speed of deep learning models, normalization of the dataset is recommended. A standard approach is to apply zero-mean and unit standard deviation normalization. The mean and standard deviation values for the dataset in the red, green, and blue channels are as follows: mean = [0.550, 0.565, 0.568], standard deviation = [0.082, 0.082, 0.085].