ViSnow: Snow-Covered Urban Roads Dataset for Computer Vision Applications

Abstract

Road surface condition estimation is an important task in fields related to transportation systems and road maintenance, especially in adverse weather conditions such as snowfall. In this paper, we introduce an image dataset for snow-covered roads in an urban context. The dataset is an extensive collection of images captured by traffic monitoring cameras in Montreal, Quebec, Canada, during the winters of 2022 and 2023. We detail the process of acquiring the dataset, including the source and the methodology to enable the replication of such a process. We also present an exploratory dataset description to showcase its rich contextual representation of the urban winter scene at different times, locations, and weather conditions. We also establish a benchmark problem for the dataset that consists of automating its annotation process. This process should add value to the dataset by attributing a label describing the snow level covering the road for each image. Finally, we discuss potential applications the dataset can enable in fields such as transportation and winter road maintenance.