This study proposes a road-change detection method, STSNet, that integrates the Swin Transformer. First, the model utilizes the Swin Transformer as the backbone network and employs a dual network structure with shared weights and a window self-attention mechanism to efficiently capture long-range dependencies. A gradient-aware multi-scale feature fusion module is next designed to merge changing gradient information with the multi-scale features, further enhancing the model's ability to obtain global change information and recognize edge features, thereby addressing the issue of blurred contours of changing targets. Finally, the scale-aware stripe attention module adaptively integrates features from the encoder and decoder to effectively combine local information and reduce the model's missed detection rate. This study used the self-made LNTU_SCD_GF and WRCD datasets for training and testing, respectively. The results demonstrate that the STSNet change detection method outperforms five comparative methods in terms of F₁ value, intersection ratio, and recall, particularly excelling in small-scale road-change detection.