# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
## 3 Results
### Comparisons with Other models  
To evaluate the validity of our model, these results were compared with those of other state-of-the-art models. For the sake of clarity, it should be mentioned that the authors trained these models in the same manner as described in Section 3.1.  
To make a meaningful comparison, the models that were considered were models that generally produced good results in semantic segmentation tasks. Because the proposed model is a combination of U-Net [31] and ResBlocks [26; 27], it was most suitable to compare the proposed model with Res-Unet [33] and U-net [31]. Table 3 and Figure 4 show the evaluation metrics (OAA and DSC) for the U-Net and Res-Unet models and for the proposed model.  
Figure 3: Comparisons of (**a**) the original images, (**b**) the ground truth images, and (**c**) the predictions of the proposed model.  
The proposed model achieved a better OAA and DSC than the Res-Unet and U-net models. Figure 5 presents a more detailed explanation and analyses. It shows the differences among the predictions that were made by Res-Unet, U-Net, and the proposed models. Figure 5(1) shows good predictions for the intersecting roads for U-Net and the proposed model, while the Res-Unet model was less successful in this task. Figure 5(2) shows that the proposed model performed significantly better than both the U-Net and Res-Unet models in predicting different types of roads, which had different pavement colors in the original image. In Figure 5(3), Res-Unet mistakenly recognized background pixels as the road class, while U-Net and the proposed model predicted them correctly. For intersecting roads, the three models performed similarly in making predictions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **OAA** & **DSC** \\ \hline Res-Unet & 96.96\% & 81.73\% \\ U-Net & 97.52\% & 84.84\% \\
**Our Model** & **98.35\%** & **90.97\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison results.  
Figure 4: Comparisons of the OAAs and DSCs for (**a**) the Res-Unet and (**b**) the U-Net.  
Figure 5(4), which captured local roads, shows the predictions of different types of roads that had different pavement colors in the original image. The U-net model provides better results than the proposed model, and the Res-Unet model failed to make a correct prediction.  
Figure 5(5) shows a case in which our model clearly outperformed both Res-Unet and Unet. While these two models predicted different parts of parking lot pixels as road pixels, our model predicted almost all these pixels as the background class.  
The evaluation metrics that are shown in Table 3 and Figure 4 combined with The results that are shown in Figure 5, suggest that the proposed model produces generally better results over both Res-Unet and U-Net for extracting road networks.  
Figure 5: Comparisons among (**a**) the original images, (**b**) the ground truth images, (**c**) the predictions of Res-Unet, (**d**) the predictions of U-Net, and (**e**) the predictions of the proposed model. Green, red and blue represent TPs, FPs and FNs, respectively.