UGD-DLinkNet: An Enhanced Network for Occluded Road Extraction Using Attention Mechanisms and Uncertainty Estimation

Road information extracted from high-resolution remote sensing images is crucial for urban planning and traffic navigation. However, challenges such as complex and variable geometric and contextual features of roads, occlusion by trees and buildings, and labeling errors in datasets present significant difficulties. To address these issues, we propose the UGD-DLinkNet model. Specifically, attention mechanisms are employed to enhance road feature extraction in occluded and complex regions by adaptively focusing on informative spatial and channelwise cues, while model uncertainty estimation helps reduce the impact of noisy annotations by guiding learning toward more reliable predictions. First, we introduce a hybrid attention module to strengthen the encoder’s ability to capture essential features and structural information. Second, a self-attention unit is integrated into the bridging network to form a dilated convolution attention module (DCAM), improving the perception of semantic features across scales. Third, a channel attention module refines skip connections, bridging shallow and deep semantic information. Finally, we incorporate Monte Carlo dropout (MC dropout) and propose an uncertainty-guided knowledge distillation strategy to mitigate labeling noise. Experiments conducted on the Massachusetts, CHN6-CUG, and DeepGlobe benchmark datasets demonstrate the comprehensive superiority of UGD-DLinkNet. On the Massachusetts dataset, our model achieves the highest F1-score of 78.75%. On CHN6-CUG, it obtains the best overall performance with an F1-score of 76.50% and an IoU of 61.95%, exceeding the best results among recent mainstream methods by 4.68% and 2.33%, respectively. On DeepGlobe, UGD-DLinkNet also achieves top performance, with an F1-score of 81.49% and an IoU of 68.77%, highlighting its strong generalization and robustness across diverse scenarios.