  Road maintenance during the Winter season is a safety critical and resource
demanding operation. One of its key activities is determining road surface
condition (RSC) in order to prioritize roads and allocate cleaning efforts such
as plowing or salting. Two conventional approaches for determining RSC are:
visual examination of roadside camera images by trained personnel and
patrolling the roads to perform on-site inspections. However, with more than
500 cameras collecting images across Ontario, visual examination becomes a
resource-intensive activity, difficult to scale especially during periods of
snowstorms. This paper presents the results of a study focused on improving the
efficiency of road maintenance operations. We use multiple Deep Learning models
to automatically determine RSC from roadside camera images and weather
variables, extending previous research where similar methods have been used to
deal with the problem. The dataset we use was collected during the 2017-2018
Winter season from 40 stations connected to the Ontario Road Weather
Information System (RWIS), it includes 14.000 labeled images and 70.000 weather
measurements. We train and evaluate the performance of seven state-of-the-art
models from the Computer Vision literature, including the recent DenseNet,
NASNet, and MobileNet. Moreover, by following systematic ablation experiments
we adapt previously published Deep Learning models and reduce their number of
parameters to about ~1.3% compared to their original parameter count, and by
integrating observations from weather variables the models are able to better
ascertain RSC under poor visibility conditions.
