Smart Road Segmentation from Aerial Images

Road detection from satellite images plays a crucial role in enhancing map-generation efficiency, making it a valuable asset for various applications, including automotive navigation systems and emergency response systems. These abstract outlines a methodology for road extraction, emphasizing the importance of accurate and efficient techniques. The process begins with image segmentation to identify road network areas. Subsequently, a decision-making and continuity technique is applied to precisely recognize roads. Finally, a Vectorization step is employed to identify line segments representing roads. This approach accounts for the curved nature of roads, demonstrating adaptability when a fully automated system encounters challenges, necessitating a semi-automated approach. Road networks hold significant importance in diverse geospatial applications, such as cartography, infrastructure planning, and traffic routing software. Recent advancements in automatic and semi-automatic road network extraction have significantly improved extraction rates. Nonetheless, the effectiveness of road detection can be hampered by noise or low-frequency images. Furthermore, road edge detection is critical for determining road direction, obstacle placement, and assessing the size and speed of objects on the road. In this study, various road detection approaches are explored, leading to the development of a novel strategy based on the median filter. Comparative analysis against established techniques reveals substantial advantages in the proposed method, highlighting the need for more effective road detection approaches.