# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
## 4 Discussion  
U-Net has provided remarkable results in semantic segmentation tasks, and it has been combined with other architectures in many studies [33; 34; 35; 36; 37; 38]. ResNet and ResBlocks [26; 27] have also been shown to well enhance the design of deep neural networks in general, to ease training while stacking more layers in the network and to overcome the vanishing gradient problem. Numerous designs had been tested to choose the optimal design that provides the most accurate segmentation results compared to our model. Our first trial method was an auto-encoder design with ResBlocks [26; 27] in both the encoding and decoding parts. The ResBlocks [26; 27] have a similar architecture to the ResNet-18 layers in an auto-encoder structure. We trained and evaluated the model, The evaluation metrics show an OAA of 97.51% and a DSC of 85.84% (Table 4 and Figure 6), and the predictions that are shown in Figure 7c,j,p have several issues: unclear edges for the extracted roads, prediction problems in some intersecting roads, and background pixels mistakenly predicted as the road class. Figure 6: Comparisons of the OAAs and DSCs for (**a**) the Auto-encoder.ResNet, (**b**) the Full Res-Unet, and (**c**) the Res-Unet (Orig. Res.). Figure 7: Comparison of the results between the proposed model and different models: (**a**,**h**,**n**) the original images, (**b**,**i**,**o**) the ground truth images, (**c**,**j**,**p**) the predictions of the first model, (**d**,**k**,**q**) the predictions of the second model,(**e**,**l**,**r**) the predictions of the third model, and (**f**,**m**,**s**) the predictions of the proposed model. Based on the analysis mentioned above that was related to the first model, when designing our second model, we enhanced it by adding a long skip connection for U-net [31] to avoid the unsatisfactory results of the first model. In addition, an extra ResBlock with 1024 filters was included as a bridge or connection between the encoding and decoding parts. Figure 7d,k,q show that better predictions were achieved in the second trial compared with the first. The edges that were extracted for the roads have been enhanced, and the two issues related to mistakenly predicted pixels and the detection of intersecting roads were somewhat improved. The second model has an OAA of 98.30% and a DSC of 89.02% (Table 4 and Figure 6). The third model was similar to the proposed model, but the ResBlocks were designed following the original model that was described by [PERSON] et al. [24]. In Figure 7e,l,r, we can see that the predictions that it produced were similar to (e,l) or slightly improved upon (r) the second model. The results show an OAA of 98.33% and DSC of 89.23% (Table 4 and Figure 6). After analyzing all the predictions from the three models that were mentioned above, the final model that was used was the proposed one, as shown in Figure 1. Figure 7 compares the predictions of the proposed model with those of the previous models. The OAA and DSC of the proposed model were 98.35% and 90.79%, respectively (Table 4 and Figure 6). The proposed model achieved the best prediction and results for the road extraction task in comparison with earlier models. The evolution of these models clarifies the importance of the combination of the skip connection for U-Net [29], the ResBlocks [24; 25], and the plain convolutional layers. The combination provides a simple shape yet an effective architecture. Although the proposed model is simple, it helps to efficiently and accurately extract the road networks, and it achieved the goal of this paper. The current results of our study are the first step to continuing our work to develop a real-time road extraction algorithm that can be embedded in any machine. The second step will be extracting the centerline of the road. In combination with the result of the first step for the road extraction, this algorithm will help to estimate the approximate width of the road and the type of the road. This step has huge importance in city planning, the  
work.