Raster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. But due to those overlapping features, it's not an easy task to extract roads from those maps. Many methods use user input to achieve this goal. In this paper we focus on an automated method to extract roads by using color segmentation and linear features detection to search for seed points having a high probability to belong to roads. Those seeds are then expanded before choosing to keep or to discard the extracted element.