Building and expansion of an efficient transportation network are essential for urban city advancement. However, tracking road development in an area is not an easy task as city planners do not always have access to credible information. A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation. Road detection is followed by computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images. The custom dataset used consists of images collected from an urban settlement in India. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.