## 4 Conclusions  
This paper proposes a lightweight Cross-Domain Feature Fusion Network (CDFFNet) to address the negative impact of areas with significant grayscale variations, such as tree shadows and road edges, on road extraction results in remote sensing image tasks. The network introduces a frequency-domain information auxiliary branch into the spatial-domain road extraction model to fully integrate spatial and frequency-domain features, improving the accuracy and connectivity of road extraction. First, a lightweight backbone network, MobileNetV2, is used to replace Xception in the DeepLabV3+ framework, reducing the model's parameter count, lowering computational complexity, and maintaining efficient feature extraction capabilities. The Atrous Bottleneck Pyramid Module (ABPM) is used for multi-scale feature extraction to learn deep spatial domain features. The Frequency Band Feature Separator (FBFS) performs frequency-domain feature mapping (low-frequency and high-frequency) using Haar wavelet transform to obtain additional frequency-domain information. Then, through the Domain Fusion Module (DFM) module, the information transfer between cross-domain feature maps is enhanced, and spatial-frequency features are selectively fused. The final experimental results validate the superiority of the CDFFNet architecture and the effectiveness of each improvement. Compared to traditional models, CDFFNet not only achieves significant improvements in accuracy but also effectively reduces memory requirements and substantially boosts inference speed. By striking a good balance among parameter count, accuracy, and inference speed, CDFFNet offers crucial advantages and flexibility for the practical deployment of road extraction models. This optimization enables CDFFNet to operate efficiently on resource-constrained edge devices, reducing hardware costs and power consumption while maintaining high-precision road extraction capabilities. Therefore, CDFFNet is particularly suitable for tasks requiring efficient and accurate road extraction, such as urban planning, disaster management, and traffic monitoring. In the future, we will improve CDFFNet for different edge devices to enhance its application value. Figure 9: The prediction results for roads in shadowed areas. RS, Remote Sensing; GT, Ground Truth. Before: The Prediction results before adding frequency domain features. After: The Prediction results after adding frequency domain features. **Author Contributions:** Conceptualization, L.G. ; methodology, L.G. ; data curation, T.S. ; investigation, T.S. ; writing--original draft preparation, T.S. ; writing--review and editing, L.Z. ; supervision, L.Z. ; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** Liaoning Province Applied Basic Research Program (YouthSpecial Project, 2023)H2/ 101600038); ShenyangYouth Science and Technology Innovation Talent Support Program (RC220458); Basic Research Special Funds for Undergraduate Universities in Liaoning Province (Guangcuan Program of Shenyang Ligong University (SYLUGXRC202216)); Basic Research Special Funds for Undergraduate Universities in Liaoning Province (LJ212410144067). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The CHN6-CUG Dataset is publicly accessible at [[http://cugurs547.mikecrm.com/ZtMn5](http://cugurs547.mikecrm.com/ZtMn5) tR]([http://cugurs547.mikecrm.com/ZtMn5](http://cugurs547.mikecrm.com/ZtMn5) tR) (accessed on 1 August 2024) and the Massachusetts Dataset is publicly accessible at [[https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)]([https://www.cs.toronto.edu/~vmnih/data/](https://www.cs.toronto.edu/~vmnih/data/)) (accessed on 1 August 2024). **Acknowledgments:** We would like to express our sincere gratitude to the editor, associate editor, and anonymous reviewers for their insightful feedback and constructive suggestions.