Road information plays a vital role in the development of rural and urban areas. Road information is considered to be an important for providing better connectivity in the areas. Mapping the road can be useful for planning and developing rural and urban areas. Moreover, road extraction from aerial and Unmanned Aerial Vehicle (UAV) imagery has been an active research topic over a few years due to many key applications like Transportation system, Geographic information system (GIS) application, etc. According to the real challenges like complex road structure usually consist of a form of heterogeneity. Some type of obstacles during road class extraction either in the form of shadow occlusion or visual occlusion occurs in satellite images. Dataset preparation is very important and also their relevance with the objective of road extraction. Due to recent advancements in aerial and UAV technology, High-Resolution UAV images are widely available which is quite impactful to extract the road networks. Extracted features from the UAV images are being utilized to detect the road class which utilizes the road properties. Road extraction methods are classified in some major categories viz. traditional methods, computer vision, ensemble methods and deep learning based methods. In the traditional methods, road extraction is evaluated based on feature level, template matching, edge and filter methods. Road monitoring is also an important factor for the transportation system and urban planning. Thus, this chapter aims to provide detail review on extraction and monitoring of the roads from aerial and UAV images using different type of approaches.