Automatic road extraction from satellite imagery is a critical task in remote sensing and urban planning, with applications in transportation network analysis, infrastructure development, and smart city solutions. This paper proposes a novel methodology for road detection by integrating object-oriented deep learning algorithms, specifically combining the Faster R-CNN architecture with the Multi-Task Road Extractor model to enhance road identification accuracy. The study utilizes SpaceNet satellite imagery data, focusing on urban areas, to train and evaluate the models. The Faster R-CNN model is employed to detect candidate road regions, while the Multi-Task Road Extractor model refines these detections by leveraging a shared encoder to perform simultaneous road segmentation and classification tasks. Experimental results demonstrate the effectiveness of this integrated approach, achieving an average precision (AP) of 0.557 at a 0.6 intersection-over-union (IoU) threshold with Faster R-CNN and a 98% accuracy after refinement with the Multi-Task model. These results highlight the potential of combining multi-task learning and object detection for improved road extraction in complex urban environments.