# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
## 3 Results  
### Training and Implementation Details  
The model is implemented using the Keras framework [39] operating on a Microsoft Windows 10 computer with one NVIDIA GeForce GTX 1070 GPU. The optimizer that was used during training is Adam [40], and the learning rate is set at 0.0001. To maximize the model's efficiency, the loss or error should be minimized. Because the task is binary segmentation, the binary cross-entropy is used as a loss function (Equation(1)): [41; 42]  
\[L(y,p(y))=-\frac{1}{N}\sum_{i=0}^{n}(y*\text{log}p(y))+(1-y)*\text{log}(1-p(y)) \tag{1}\]  
where \(p(y)\) is the predicted value, \(y\) is the true label, and \(N\) is the number of samples.  
To assess the performance efficiency of the proposed model, two evaluation metrics were used. The first metric is the overall accuracy (OAA), which is defined in Equation (2) [43; 44]. The definitions of TP, TN, FP, and FN are shown in Table 1.  
\[OAA=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]The other metric is the Dice similarity coefficient (DSC) [45; 46; 47; 48], which is also known as [PERSON]'s binary index, Zijdenbos' similarity index, or the F1 score [46]. The DSC is a metric to measure the segmentation performance, and it is shown in Equation (3). If the DSC is equal to 1, the road is perfectly segmented. Conversely, if the DSC is equal to 0, the roads are completely missegmented.  
\[DSC=\frac{2 TP}{2 TP+FP+FN} \tag{3}\]  
It is preferable to use more than one metric in the evaluation process. The OAA uses the TN, while the DSC does not. The DSC counts the true positives (TPs) twice, and it is more suitable for evaluating the results for imbalanced datasets. It should be mentioned that the authors do not use a pretrained model in the training process, and our model is trained from scratch. The results of the evaluation metrics in Equations (2) and (3) are shown in Table 2 and Figure  
The proposed model provides good results according to the evaluation metrics with a 98.35% overall accuracy and a 90.97% DSC.  
According to Figure 3(1), the proposed model produces a reasonable prediction that is comparable to the ground truth image. It should be mentioned that the labeling of the ground truth images includes two problems: the shoulders of the roads in the true labels are sometimes marked as part of the road and sometimes they are not (Figure 3(2)-(4)), and some side roads or local roads are incorrectly labeled in the ground truth image (Figure 3(5)). The first problem increases the difficulties of extracting clear edges for the road networks, but our model produced correct predictions in most of the images, as shown in Figure 3(2)-(4). The proposed model overcame the second problem, as shown in Figure 3(5).  
With respect to the problems of the labels that were mentioned above, it is very difficult to observe these problems, even after cropping the images into small sizes, and subsequently, it is very difficult to correct them. These problems are very common when labeling datasets because these labels are manually set, and they are prone to human errors.  
Figure 2: Evaluation of the proposed model in terms of (**a**) the overall accuracy, (**b**) the DSC, and (**c**) the loss.