The amalgamation of Unmanned Aerial Vehicle (UAV) based systems with models built on Artificial Intelligence (AI) and Computer Vision approaches have enabled several applications in urban planning and smart cities, such as remote health monitoring of roads and infrastructure. However, most of such existing models are trained and evaluated for clear lighting conditions, and they do not perform well under low visibility. This work proposes a fast and lightweight approach for deployment on UAV-based systems that can (i) detect the low-visibility condition in a road image captured by a UAV, and (ii) alleviate it and enhance the quality of the road image. The proposed approach achieves state-of-the-art results and thus establishes itself as an essential precursor to downstream Computer Vision tasks related to remote monitoring of roads, such as identification of different distress conditions.