Automated road network extraction from satellite imagery represents a critical advancement for Geographic Information Systems (GIS) applications in infrastructure management and urban planning. This paper introduces two novel deep learning architectures based on LinkNet: RoadNet-MS (Multi-Scale) and RoadNet-AUX (Multi-Scale with Auxiliary Supervision), specifically designed to enhance road segmentation performance. RoadNet-MS incorporates Multi-Scale Contextual Blocks (CMS-Blocks) and hybrid blocks to effectively capture diverse contextual features at multiple scales, achieving F1-scores of 78.87% on the challenging DeepGlobe dataset and 82.30% on the Boston & Los Angeles dataset. RoadNet-AUX extends this architecture through auxiliary supervision, further improving performance with F1-scores of 79.14% on DeepGlobe and 82.33% on Boston-LA. Both proposed architectures demonstrate competitive performance and consistent improvements over existing methods, including the state-of-the-art NL-LinkNet, across both evaluation datasets. Notably, RoadNet-MS achieves the highest precision (83.55%) among all compared methods on DeepGlobe. These contributions provide a pathway toward more accurate and scalable road network mapping, essential for modern urban planning and infrastructure monitoring applications.