# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 1 Introduction
### Innovation Statement  
To surmount the persistent challenges associated with road extraction in high-resolution remote sensing imagery, researchers have devised a plethora of deep learning methodologies tailored specifically to remote sensing applications. Among these, the structurally refined PCCAU-Net stands as a recent breakthrough, incorporating Pyramid Pathway Input, CoordConv convolution, and Dual Input Cross Attention (DCA) modules. This innovative amalgamation facilitates the more efficacious capture and integration of multi-scale features, thereby augmenting both the accuracy and robustness of road extraction.  
Distinctive contributions and attributes of our model are as follows:  
1. Multi-scale feature and spatial context fusion: PCCAU-Net employs Pyramid Pathway Input and CoordConv convolution, achieving a seamless assimilation of features from across multiple scales down to fine-grain levels, along with precocious spatial context recognition. This holistic design strategy assures precise road localization and extraction, even amidst complex settings.
2. At the critical encoder stage, the model incorporates a Dual-Input Cross Attention (DCA) module. This not only augments the nimble integration of feature maps across various scales but also sharpens the model's focus on road center detection, effectively attenuating false positives induced by obstructions, entanglements, and noise.
3. Across key performance metrics such as precision, recall, and Intersection-over-Union (IoU), PCCAU-Net demonstrates a clear supremacy over existing mainstream approaches, vindicating its potential applicability in the realm of remote sensing data processing.  
This manuscript elucidates the design principles, empirical validations, and potential applications of this model, aiming to offer substantive insights for future endeavors in remote sensing data processing. The article is structured as follows: The introduction delineates the backdrop and challenges of road extraction in high-resolution remote sensing imagery and elucidates why traditional deep learning models may encounter difficulties. Subsequently, we present our solution, an innovative deep learning model named PCCAU-Net, endowed with Pyramid Pathway Input and Dual Input Cross Attention (DCA) modules. In the Methods section, we provide a comprehensive description of the PCCAU-Net architecture and its operational mechanics, including its cornerstone components like CoordConv and DCA modules. In the Results section, we introduce the two road datasets selected for the experiment, Massachusetts Roads Dataset and DeepGlobe Road Dataset, which provide rich image data for road-related computer vision tasks. Secondly, through comparative analysis and ablation experiments, various performance indicators are compared to demonstrate the performance of our model on these two primary remote sensing datasets, confirming its effectiveness and superiority. The Discussion section provides a detailed review of the experimental results and analyzes the advantages and potential limitations of PCCAU-Net. Finally, the conclusion summarizes the key contributions and discoveries of this study, pointing to directions for future research and potential improvements.