# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 1 Introduction  
### Related Works  
In the swiftly advancing field of remote sensing technology, we now have unprecedented access to high-resolution images of the Earth's surface, offering invaluable insights into geography [1], ecology [2] and urbanization [3]. Despite these advances, the processing of high-resolution remote sensing images presents myriad challenges, particularly in applications like road extraction, building detection, and land use classification [4; 5]. Accurate, rapid, and automated road information extraction from these images is pivotal, not only for smart city construction, transportation planning, and autonomous driving technologies, but also plays a crucial role in emergency management [6], environmental monitoring [7], and military reconnaissance [8]. Traditional remote sensing image processing methods mainly use template matching approaches [9], knowledge-driven approaches, object-oriented approaches [10], and path morphology approaches [11]. The breakthroughs in computer vision via deep learning, especially convolutional neural networks (CNNs), have ushered in new opportunities for remote sensing image processing [12]. CNNs autonomously learn representative features from data, thus achieving impressive performance across diverse tasks. However, conventional CNN architectures are not entirely equipped to handle the unique challenges presented by high-resolution remote sensing images, such as large homogeneous areas, complex background structures, and intricate target features [13]. Subsequent innovations like Fully Convolutional Networks (FCNs) adapted CNNs for pixel-level segmentation by replacing fully connected layers with convolutional layers [14], but limitations such as a loss of detail due to continuous down-sampling remain unresolved [15]. U-Net [16] introduced skip connections for the first time. Its encoder reduces the spatial dimensionality and captures spatial detail information, while the decoder reuses the low-level features from the encoding stage to gradually restore the input size and recover spatial location information. Since then, people have continuously made improvements to the U-Net architecture to alleviate these challenges and solve the gradient problem through residual structures [17]. Hybrid networks like ResU-Net have further combined the advantages of U-Net and Res-Net, stabilizing gradients while facilitating information exchange through shortcut connections [18]. Techniques like dilated convolutions in DeepLab series networks have allowed for multi-scale feature learning while expanding the receptive field [19; 20; 21; 22]. This method utilizes dilated convolutions instead of up-sampling and combining dilated convolutions with atrous spatial pyramid pooling modules to augment the receptive field and obtain multi-scale features and enhance the model's multi-scale prediction capabilities. However, the feature extraction method based on dilated convolutions can easily lose information related to small-scale targets as the convolution kernels solely extract features from restricted regions. [PERSON] et al. [23] introduced the HRNet, which employs a multi-branch structure to simultaneously maintain high- and low-resolution features and repeatedly performed multi-scale fusion to generate rich high-resolution representations. Nevertheless, in occluded images, some resolutions may exhibit information deficits due to partial regions being obscured, ultimately affecting the effectiveness of the resulting feature maps. In parallel, attention mechanisms have been introduced in machine translation tasks [24]. [PERSON] proposed the Convolutional Block Attention Module (CBAM) based on the attention mechanism in 2018; this is a lightweight module that pays attention to the importance of both space and channel [25]. [PERSON] obtained the contextual information of the road through global attention, used core attention to obtain multi-scale information, improved the connectivity of road extraction, and restored the information at the occlusion place.