A lightweight road segmentation algorithm for aerial imagery, termed ARSNet, was proposed to address the issue that roads in aerial images often suffer from structural ambiguity and discontinuous edges. An orthogonal feature fusion module (OFFM) was designed to achieve interaction and enhancement among features in different orientations. A context-guided feature aggregation module (C2f-CGA) was introduced to improve the continuity of road boundary and topological structure extraction. Large-kernel separable attention was introduced into the feature spatial pooling module in order to strengthen the collaborative perception of road features across various scales and directions. A layer-adaptive sparse pruning strategy based on weight magnitude was employed to reduce the number of parameters to 22.5×10⁶ with FLOPs controlled at 14.8×10⁹. Experiments conducted on the public Massachusetts and DeepGlobe datasets showed that ARSNet achieved precision of 78.12% and 79.78%, recall of 79.63% and 81.63%, intersection-over-union of 64.85% and 67.88%, and average path length similarity of 74.95% and 75.53%, respectively. ARSNet achieved an inference speed of 178 frames per second while maintaining high accuracy, consistently outperforming a series of state-of-the-art algorithms.