Abstract: The existing remote sensing image segmentation methods lack the coordinated processing of low-frequency structures and high-frequency details during cross-scale feature fusion, and cannot adaptively respond to image content, making it difficult to effectively solve the problem of large intra-class variations and small inter-class variations in high-resolution remote sensing images caused by natural changes, illumination, and shadow interference. To address these problems, an adaptive frequency-aware network (AFANet) is proposed and implemented. First, a frequency dynamic fusion module is proposed, which suppresses high-frequency noise components while retaining low-frequency structures through adaptive low-pass and high-pass filter, and enhancing high-frequency detail boundary information. Secondly, a dual-domain learning block is constructed to integrate spatial and frequency information to achieve joint modeling of local details in the spatial domain and global structures in the frequency domain. Finally, a detail-enhanced module is introduced to enhance the feature extraction and generalization capabilities of the model using different differential convolutions. The quantitative analysis and visualization results of the comparative and ablation experiments on the two classic public datasets, Vaihingen and Potsdam, show that AFANet outperforms seven state-of-the-art segmentation methods in terms of F1 score, OA and mIoU, with the mIoU reaching 85.13% and 87.81% respectively, verifying the superior performance of AFANet.