A novel approach for automated road network extraction from multispectral WorldView-2 imagery using a knowledge-based system is presented. This approach uses a multispectral flood-fill technique to extract asphalt pixels from satellite images; it follows by identifying prominent curvilinear structures using template matching. The extracted curvilinear structures provide an initial estimate of the road network, which is refined by the knowledge-based system. This system breaks the curvilinear structures into small segments and then groups them using a set of well-defined rules; a saliency check is then performed to prune the road segments. As a final step, these segments, carrying road width and orientation information, can be reconstructed to generate a proper road map. The approach is shown to perform well with various urban and suburban scenes. It can also be deployed to extract the road network in large-scale scenes.