## 2 Related Work  
Applications of deep learning models have advanced the field of road extraction from satellite imagery. [PERSON] and [PERSON]'s [1] pioneering work on neural network-based road extraction laid the foundation for subsequent advancements. Their model demonstrated the potential of large neural networks to handle extensive datasets for road detection. U-Net [21], a well-known model recognized for its efficacy in semantic segmentation, has been widely adapted and extensively applied to road extraction in the recent literature. [PERSON] et al. [5] introduced Deep Residual U-Net by incorporating residual units into the U-Net architecture, achieving better performance in road extraction compared to its predecessors, including the original U-Net [21] and Mnih-CNN [1]. [PERSON] et al. [22] proposed a road-extraction deep learning method called DenseUNet, which takes advantage of the U-Net skip connection architecture and dense connections within dense units. DenseUNet is capable of focusing on foreground pixels and achieving comparable results in road extraction by surpassing shadow occlusions to a limited extent. [PERSON] et al. [23] proposed the Complement UNet (C-UNet) model for road extraction from satellite imagery. The method first employs a standard UNet to extract road information, followed by an erasing procedure that removes partially extracted road areas based on a fixed threshold. The erased regions are then processed by Multi-scale Dilated UNet (MD-UNet) to extract finer details, and the outputs from UNet and MD-UNet are fused to produce high-quality road extraction results. Building upon the importance of leveraging prior contextual information through U-Net and spatial structural associations for road extraction, [PERSON] et al. [24] introduced SDUNet, which integrates densely connected blocks with spatial CNN capabilities. The model leverages a structure-preserving module to enhance the extraction of continuous road features by incorporating spatial context in four directions, achieving improved performance in road network extraction. Continuing the chronological advancements of U-Net, [PERSON] et al. [25] introduced enhancements like a patch-based attention mechanism and rotation-based augmentation to the original U-Net. These innovations led to an advanced-attention U-Net that outperformed earlier methods discussed in this section. Similarly, LinkNet [26] has been effectively utilized for high-quality road extraction due to its lightweight design and the integration of residual connections. D-LinkNet, proposed by [PERSON] et al. [27], extended the LinkNet architecture by incorporating a pretrained ResNet34 model [19] as the encoder and adding dilated convolution layers in the central part to enhance the receptive field while preserving spatial details. The decoder remained similar to the original LinkNet, maintaining computational efficiency. One of the recent models for road extraction based on LinkNet is RFE-LinkNet, proposed by [PERSON] et al. [28]. It incorporates receptive field-enhancement modules to capture long-range dependencies and preserve spatial details. Also, the Channel Attention Module (CAM) and Spatial Attention Module (SAM) have been employed to refine multi-scale features, resulting in improved performance in road extraction. While the previously mentioned CNN models have demonstrated strong performance in road extraction from satellite imagery, they still face limitations due to the intricate nature of roads. A promising alternative for achieving accurate and high-quality road extraction is the implementation of the DeepLab series. DeepLabV3 [12], for instance, employs atrous convolution within the ASPP module, achieving better results through parallel atrous convolutions adjusted by the output stride, primarily in general semantic segmentation tasks. DeepLabV3+ [13], an extension of DeepLabV3, further enhances segmentation accuracy at object boundaries by incorporating a decoder module. [PERSON] et al.