###### Abstract  
In this study, we address the limitations of current deep learning models in road extraction tasks from remote sensing imagery. We introduce MixerNet-SAGA, a novel deep learning model that incorporates the strengths of U-Net, integrates a ConvMixer block for enhanced feature extraction, and includes a Scaled Attention Gate (SAG) for augmented spatial attention. Experimental validation on the Massachusetts road dataset and the DeepGlobe road dataset demonstrates that MixerNet-SAGA achieves a 10% improvement in precision, 8% in recall, and 12% in IoU compared to leading models such as U-Net, ResNet, and SDUNet. Furthermore, our model excels in computational efficiency, being 20% faster, and has a smaller model size. Notably, MixerNet-SAGA shows exceptional robustness against challenges such as same-spectrum-different-object and different-spectrum-same-object phenomena. Ablation studies further reveal the critical roles of the ConvMixer block and SAG. Despite its strengths, the model's scalability to extremely large datasets remains an area for future investigation. Collectively, MixerNet-SAGA offers an efficient and accurate solution for road extraction in remote sensing imagery and presents significant potential for broader applications. high-resolution remote sensing imagery; road extraction; MixerNet-SAGA; ConvMixer blocks; scaled attention mechanisms; deep learning architectures +
Footnote †: journal: Medical  
high-resolution feature maps [10]. This design aids in generating precise object boundaries in segmentation results and showcases remarkable multi-scale feature fusion capabilities. To tackle accuracy degradation with increased network depth, [PERSON] et al. (2015) introduced the ResNet structure [11]. ResNet, with its residual connections, permits the training of profoundly deep neural networks, mitigating gradient vanishing issues and augmenting model performance, rendering it suitable for various image tasks, including segmentation. To synergize multi-scale information during training, [PERSON] et al. advanced the DeepLab series, employing Deep Convolutional Nets for semantic image segmentation [12]. By incorporating Atrous Convolution and Conditional Random Field (CRF) techniques, DeepLab not only enhances segmentation quality but also elevates accuracy by fusing multi-scale convolutional feature maps with the global context. Building on the foundation of DeepLab v1, [PERSON] and team introduced the ASPP module, which augments segmentation outcomes [13]. However, challenges such as elevated computational complexity and limited performance improvement persist. Prioritizing multi-scale information retention for detailed task management, [PERSON] et al. proposed HRNet in 2019 [14]. Attention Mechanism in Road Extraction: In 2014, [PERSON] et al. pioneered the incorporation of attention mechanisms in machine translation tasks, enabling models to \"focus\" on various segments of input sequences by attributing distinct weights to different positions [15]. Since then, this mechanism has seen extensive applications across various deep learning tasks [16]. MHA-Net employed attention mechanisms in segmentation tasks to manage multi-scale information, directing the model to efficiently capture key regional features [17]. In 2020, [PERSON] and colleagues proposed EMANet, utilizing attention mechanisms to integrate features across scales, thereby enhancing the model's capacity to discern semantic information at various scales. However, the efficacy of attention mechanisms can be contingent on the quality of the input data, potentially underperforming with sub-par images. In 2021, [PERSON] and team introduced scale-independent self-attention (ScaNet), gaining significant traction [18]. This innovation permits the network to autonomously adjust feature weight mechanisms across spatial scales, hence better capturing long-range relationships within images.