This research investigates the application of the DINO (Distillation with No Labels) framework, a self-supervised learning approach, for efficient road and pothole segmentation. By integrating a DINO-enhanced ResNet-50 backbone with a U-Net model, this study addresses segmentation challenges in dynamic environments. The framework employs momentum encoders, multi-crop training, and stability mechanisms to facilitate robust feature extraction without requiring labeled datasets. Through strategic fine-tuning, the model achieves precise segmentation of road surfaces and potholes, making it a promising approach for real-world applications in autonomous systems and infrastructure assessment. This study further discusses model evaluation, comparison with state-of-the-art approaches, and its implications for transportation infrastructure.