# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Ablation Experiments Results and Analysis  
To rigorously evaluate both the efficacy of our proposed PCCAU-Net model and the contributions of its individual components, a series of ablation experiments were conducted. It is worth noting that while CoordConv convolution was employed in the first layer of the U-Net network, we forwent isolated ablation tests for this design, as its effectiveness had already been substantiated in the existing literature. The ablation tests were structured into five experimental setups as follows:  
Setup a (U-Net): Serving as the baseline, this setup employs the canonical U-Net architecture. It provides a performance reference and forms the basis for comparison with other configurations. Setup b (U-Net + Pyramid Pathway Input): Building upon the baseline U-Net, this setup incorporates Pyramid Pathway Input, designed to enhance the model's multi-scale feature processing for more precise road extraction. Setup c (U-Net + DCA without fusion attention): In this configuration, a stripped-down DCA module, devoid of fusion attention, is integrated into the U-Net model. This is aimed at evaluating the fundamental contribution of the DCA module to the task of road extraction. Setup d (U-Net + Complete DCA): This setup supplements the U-Net with a fully fledged DCA module, inclusive of fusion attention. The design is intended to appraise the comprehensive performance of the DCA module, and it serves as a comparative reference against Setup C to gauge the specific contributions of fusion attention. Setup e (PCCAU-Net): This is the full configuration of our proposed model, which amalgamates both the Pyramid Pathway Input and the complete DCA module. The setup aims to demonstrate the optimal performance attainable when all design elements are integrated. Through these five configurations, we not only methodically assess the effectiveness of both the Pyramid Pathway Input and the DCA module, but also gain comprehensive insights into their synergistic impact on the overall task of road extraction. The qualitative outcomes for these setups across two datasets are illustrated in Figure 7, and the quantitative findings are delineated in Table 4.  
Figure 7: The test results of the ablation experiment scheme on two public road datasets, where images 1 and 2 are from the Massachusetts Roads Dataset, and images 3 and 4 are from the DeepGlobe Road Dataset. The letters \"a, b, c, d, and e\" in Figure 7 stand for experimental setups a, b, c, d, and e, respectively. In Figure 7, extraction outcomes from multiple approaches are depicted across four images, each presenting distinct challenges. The U-Net-based Scheme a offered a rudimentary performance across all images, particularly faltering in instances of intricate road interweaving and heterogeneity within the same spectral class. Scheme b, augmented with Pyramid Pathway Input, displayed nuanced improvements in detail extraction, but remained inadequate for handling same spectrum heterogeneity. Schemes c and d, both incorporating DCA modules, markedly ameliorated the extraction of complexly intertwined roads, with Scheme d showing a discernible edge in detail fidelity. Notably, Scheme e (PCCAU-Net) consistently outperformed others across all metrics, excelling particularly in scenarios involving complex road configurations and spectral variability. In a quantitative analysis presented in Table 4, all five ablation study schemes were rigorously assessed on two distinct datasets--Massachusetts Roads Dataset and DeepGlobe Road Dataset. The letters a, b, c, d, and e shown in Table 4 have the same meanings as those in Figure 7. A detailed breakdown reveals that Scheme a (U-Net) registered the lowest aggregate performance across both datasets, especially in terms of Intersection-over-Union (IoU), displaying a conspicuous performance gap compared to other models. Scheme b (U-Net + Pyramid Pathway Input) achieved subtle yet consistent elevations across all evaluation metrics, underscoring the ancillary benefits of Pyramid Pathway Input.