This paper addresses the challenges of segmenting rural roads in remote sensing images, including small pixel proportion, irregular shapes, shadow occlusions, and blurred edges. To improve the segmentation accuracy of small and single-object rural roads, we propose an improved DeepLabV3+ semantic segmentation model. We employ MobileNetV3 as the backbone for parameter reduction and enhanced accuracy. A global attention mechanism is incorporated to improve global information extraction and generalization. Depthwise separable convolutions replace standard convolutions in the ASPP module to minimize information loss and computational cost. Experiments on a self-built satellite remote sensing road image dataset demonstrate significant improvements, achieving an MIoU of 84.45% and MPA of 92.32%, outperforming the original DeepLabV3+ by 4.63% and 6.48%, respectively, with a parameter size of only 6.30×106. Validation on the public CHN6-CUG dataset confirms the model′s effectiveness, showing MIoU and MPA improvements of 3.05% and 5.54% to reach 79.64% and 88.13%, respectively. These results indicate that our lightweight, improved model effectively enhances rural road segmentation accuracy and efficiency.