## 1 Introduction
[23] introduced RCFS-Net, which combines contextual information into a full-stage feature fusion module to extract roads in tree- and building-obscured scenes. [PERSON] et al. [24] constructed a U-Net-like model structure by incorporating the idea of residual connections from ResNet [25], utilizing residual connections to reuse early features, which effectively balanced the model's parameter count and performance. This approach mitigated issues such as blurred road edges and loss of details in road extraction caused by complex backgrounds and variations in lighting. [PERSON] et al. [26] used recursive CNN (RCNN) units in the U-Net architecture in order to simultaneously complete the road segmentation and centerline tracking tasks, which reduces the disconnection of the road results.The DeepLabV3+ used the atrous convolution to propose an ASPP module that enhances the model's ability to capture long-distance information without increasing computational effort. Based on the DeepLabv3+ model, [PERSON] et al. [27] introduced a feature map slicing module in Encoder to make the model focus more effectively on small target objects in localized areas in remote sensing images and added an attention mechanism module to make the model pay more attention to effective feature information in the image. [PERSON] et al. [28] and [PERSON] et al. [29] incorporated the ASPP (Atrous Spatial Pyramid Pooling) module after feature extraction using the encoder-decoder architecture, further extracting multi-scale contextual information from the images and enhancing the segmentation accuracy of road edges. In the overall extraction strategy, [PERSON] et al. [30]enhanced the reliability of the extraction results by comprehensively utilizing two graph reasoning approaches in different spaces. They estimated the graph's structure of image features through a similarity matrix and then applied Graph Convolutional Network (GCN) for feature extraction. Simultaneously, they simplified the relationships among multiple different regions in the coordinate space into a fully connected graph and used GCN to model the relationships between corresponding features. Wavelet transform plays a crucial role in signal and image processing by capturing multi-resolution representations of the low and high frequency components of a signal or image to better understand their structure and characteristics. The combination of CNN and wavelet transform has been widely applied in image denoising, change detection, and remote sensing segmentation. [PERSON] et al. [31] proposed a novel network architecture integrating CNN with wavelet transform, which effectively extracts and fuses multi-scale features to capture subtle changes and structural information of sea ice. [PERSON] et al. [32] used wavelet transform to decompose pediatric echocardiographic images into high-frequency components containing image details like cardiac boundaries and low-frequency components representing overall cardiac contours. Through multi-level feature fusion, the model enhances its capability to capture the complex structures and details of medical images. In the complex background of remote sensing images, areas with significant grayscale variations, such as road edges and shadows caused by tree occlusions, often lead to segmentation errors. Frequency domain features are more sensitive to such areas [33; 34]. Therefore, to fully leverage the advantages of both spatial and frequency domain information, this study implements effective extraction and integration of features between the spatial and frequency domains. This study adopts DeepLabV3+ as the main framework and designs a Cross-Domain Feature Fusion Network (CDFFNet) for road extraction. The model retains DeepLabV3+'s capability of extracting shallow and deep spatial features and multi-scale features, while introducing additional frequency domain features and effectively integrating them. First, the lightweight MobileNetV2 [35] replaces the original DeepLabV3+ backbone, the modified Xception [36], to extract image features.