The presence of roadworks greatly affects the validity of prior maps used for navigation by autonomous vehicles. This paper addresses the problem of quickly and robustly assessing the gist of traffic scenes for whether roadworks might be present. Without explicitly modelling individual roadwork indicators such as traffic cones, construction barriers or traffic signs, our method instead only exploits the engineered visual saliency of such objects. We draw inspiration from opponent colour vision in humans to formulate a novel roadwork scene signature based on an opponent spatial prior combined with gradient information. Finally, we apply our roadwork scene signature to the task of roadwork scene recognition, within a classification framework based on soft assignment vec-torization and RUSBoost. We evaluate our roadwork signature on real life data from our autonomous vehicle.