# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Comparative Experiment Results and Analysis
#### 3.3.1 Analysis of Massachusetts Roads Dataset Results  
Within this section, we undertake a comprehensive evaluation of the PCCAU-Net model using the Massachusetts Roads Dataset. The assessment is bifurcated into qualitative and quantitative dimensions. Firstly, the qualitative analysis employs visual comparisons to elucidate how PCCAU-Net fares against other advanced models in road extraction tasks. Secondly, the quantitative facet deploys a set of standard metrics to numerically gauge the model's performance. The synthesis of these evaluations aims to delineate both the advantages and potential of PCCAU-Net in high-resolution remote sensing road detection tasks. Qualitative results are illustrated in Figure 5, while quantitative outcomes are tabulated in Table 2.  
Figure 5 showcases the performance of five models--U-Net, Residual U-Net (abbreviated as ResU-Net), DeepLab V3+, SDUNet, and our proposed PCCAU-Net--across five different scene captures from the Massachusetts Roads Dataset. These scenes represent a variety of intricate scenarios commonly encountered in remote sensing imagery. Scene 1 poses the particular challenge of severe spectral similarity between houses and roads, a hallmark issue in high-resolution remote sensing. While most models are susceptible to misclassifications in such contexts, PCCAU-Net demonstrates enhanced robustness. Scene 2 predominantly features a roundabout, which is traditionally a complicating factor in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **IoU (\%)** \\ \hline U-Net & 78.823 & 83.764 & 77.672 \\ \hline Resunet & 79.297 & 83.253 & 77.902 \\ \hline DeeplabV3+ & 79.567 & 83.909 & 77.457 \\ \hline SDUNet & 80.094 & 84.856 & 78.625 \\ \hline PCCAU-Net & 83.395 & 84.076 & 78.783 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative Results Evaluation of Five Models on Massachusetts Roads Dataset. Figure 5: Extraction results of five models on the Massachusetts Roads Dataset. remote sensing analyses. PCCAU-Net manages to differentiate it accurately, despite its complex morphology. Scene 3 illustrates the issue of delineating dual carriageways, a task at which conventional models often fail by mistaking them for a single road; PCCAU-Net identifies them distinctly. Scene 4 examines the model's capability to recognize detailed roads, and PCCAU-Net excels in capturing these intricacies. Finally, Scene 5 focuses on a major urban road with shadow occlusions, where PCCAU-Net exhibits remarkable resilience. Collectively, Figure 5, through its multi-angular and multi-layered exposition, authentically reflects the versatility and adaptability of PCCAU-Net in dealing with a multitude of complex remote sensing scenarios. This observation not only validates the significant advantages of the PCCAU-Net model in practical applications but also accentuates its broad applicability in high-resolution remote sensing road detection tasks. Table 2 offers a comprehensive quantitative evaluation of the performance of five advanced architectures--namely, U-Net, ResU-Net, DeepLab V3+, SDUNet, and our proposed PCCAU-Net--on the Massachusetts Roads Dataset. This tabulation employs three pivotal metrics--precision, recall, and intersection-over-union (IoU)--to furnish a robust framework for assessing the relative efficacy of these models in the task of road extraction. 1. Precision: PCCAU-Net outperforms its peers with a precision rate of 83.395%, thereby indicating fewer false positives. The closest competitor, SDUNet, registers a precision of 80.094%, underscoring the substantial advantage of PCCAU-Net in minimizing identification errors. 2. Recall: While SDUNet achieves the highest recall rate at 84.856%, indicating its prowess in detecting the majority of true road segments; PCCAU-Net trails closely with a recall of 84.076%. This demonstrates a well-calibrated balance between precision and recall, offering a more nuanced view of road attributes. 3. IoU: PCCAU-Net leads with an IoU of 78.783%, narrowly eclipsing SDUNet's 78.625%.