从遥感影像中精准提取结构完整的道路网络在自动驾驶等多个领域至关重要。然而，道路的线性细长结构与周围复杂的异构背景纹理在空间域中高度耦合，导致现有方法难以在抑制噪声的同时有效捕捉长距离依赖，其提取的道路网络因而呈现碎片化和结构不完整的状态。为此，提出一种频域与空间协同增强的网络FSC-Net，该网络通过全局上下文分支与局部细节分支，实现对道路网络的精准提取。在全局分支设计了自适应频域注意力模块(AFAM)，通过频域学习以有效提取道路的细长结构特征并抑制背景相关的噪声，将提炼后的结构特征送入改进的Mamba模块中，在线性复杂度下捕捉可靠的道路特有全局结构依赖。细节分支由残差卷积块构成，用于提取道路的局部细节特征。最后通过双向引导融合模块(BGFM)聚合两个分支的互补性特征，确保生成的道路网络兼具结构完整性与边缘精确性。在Massachusetts和DeepGlobe数据集上进行一系列实验验证了FSC-Net的有效性。与其他代表性方法相比，FSC-Net能够更鲁棒地捕捉道路网络的长距离依赖关系，从而显著提升了路网提取的连续性和完整性。

Accurately extracting structurally complete road networks from remote sensing imagery is crucial in numerous fields, such as autonomous driving. However, the linear, slender structure of roads is highly coupled with complex and heterogeneous background textures in the spatial domain, making it difficult for existing methods to effectively capture long-range dependencies while suppressing noise, resulting in fragmented and structurally incomplete road networks.To address this, we propose FSC-Net, a network that collaboratively enhances features in both the frequency and spatial domains, achieving precise road network extraction through a global context branch and a local detail branch. In the global branch, an Adaptive Frequency-Domain Attention Module (AFAM) is designed to effectively extract the elongated structural features of roads and suppress background-related noise through frequency-domain learning. The refined structural features are then fed into a modified Mamba module to capture reliable global structural dependencies specific to roads with linear complexity. The detail branch, composed of residual convolutional blocks, is used to extract local detail features of roads. Finally, a Bidirectional Guidance Fusion Module (BGFM) aggregates the complementary features from the two branches, ensuring the generated road network possesses both structural integrity and edge precision.A series of experiments on the Massachusetts and DeepGlobe datasets validate the effectiveness of FSC-Net. Compared with other representative methods, FSC-Net can more robustly capture the long-range dependencies of road networks, thereby significantly improving the continuity and completeness of the extracted road network.