###### Abstract
In road extraction tasks, such scale-independent self-attention facilitates the network in proficiently identifying road continuity and curvature, enhancing extraction accuracy [19]. However, the attention mechanism also has the problem of large computational resource requirements. Aiming at this problem, some recent works have been improved. For example, RADANet proposed by [PERSON] et al. uses a combination of deformable convolution and an attention mechanism, which can better express multi-scale features, and also designs a residual structure to reduce the amount of parameters [20]. SDUNet proposed by [PERSON] et al. integrates the spatial attention module in U-Net to enhance the local details and reduce the calculation amount of the attention module [21]. [PERSON] et al. proposed a semantic segmentation framework using an adaptive channel attention module to improve the recall of road extraction [22]. [PERSON] et al. designed a dual-decoder structure, and at the same time they used the attention mechanism to enhance the expression of details and improve robustness in complex scenes [23]. In the current research Our proposed MixerNet-SAGA offers several advantages:  
1. Computational Efficiency: By amalgamating deep convolution with 1 x 1 convolution in the ConvMixer block, MixerNet-SAGA maintains superior performance while ensuring lower computational and parameter complexity. 2. Multi-Scale Feature Extraction: Its unique multi-scale attention mechanism allows for the capturing of road information across micro to macro levels, aptly adapting to remote sensing imagery of varied resolutions. 3. Enhanced Feature Representation: The combination of the ConvMixer block and multi-scale attention mechanisms facilitates efficient extraction and integration of spatial and channel information, augmenting feature expressivity. 4. Flexibility and Adaptability: The design of MixerNet-SAGA allows for seamless integration with other deep learning modules and techniques, ensuring adaptability across diverse remote sensing image processing tasks. 5. Robustness: By integrating a myriad of feature extraction and enhancement techniques, MixerNet-SAGA showcases commendable resilience in the face of intricate urban landscapes and varied road conditions. This paper is structured as follows:  
The Introduction delves into the challenges of road extraction from high-resolution remote sensing imagery and the limitations traditional deep learning models may encounter. We then present our innovative solution, MixerNet-SAGA, which holds the ConvMixer block with multi-scale attention mechanisms. In the Methods section, we elucidate the design and operation of MixerNet-SAGA, detailing core components such as the ConvMixer block and multi-scale attention mechanisms. The Experiments and Results sections showcase our model's performance on two primary remote sensing datasets and benchmarks it against other leading road extraction models. This section validates the practical efficacy and superiority of MixerNet-SAGA. The Discussion section offers a deep dive into our findings, dissecting the strengths and potential limitations of MixerNet-SAGA, juxtaposing it against its peers. Finally, in the Conclusion, we encapsulate the central contributions and insights of this paper, proposing potential future research trajectories and enhancements.