Machine Learning (ML), and especially Deep Learning (DL) methods, have evolved rapidly over the last years and showed remarkable advances in research areas such as computer vision and natural language processing; however, there are still engineering applications in industries such as transportation where DL methods have not been applied yet or that can be benefited from an integrated approach using DL in addition to other methods. For countries in Northern latitudes, one of such applications is Monitoring Road Surface Condition (RSC) during the Winter season for improving road safety and road maintenance operations. In this study, we introduce a novel approach for monitoring of RSC that integrates DL methods and Spatial Statistics (SS) to simultaneously process data from roadside cameras and weather stations to determine automatically the category of snow coverage at sample locations across a region of interest.