## 2 Data and Methods
### U-Net Model Structure  
The U-Net was initially proposed for segmenting biomedical images [40]. It was formed as the encoder (contracting), bridge, and decoder (expansive) blocks. As its name implies, it is a U-shaped architecture in which the high-resolution features are extracted by down-sampling the input image, followed by up-sampling them to recover the original spatial resolution. These features are concatenated with the up-sampled output to achieve precise localization. High-dimensional feature spaces, created by up-sampling, allow the architecture to feed the context information into higher-resolution layers. In order to achieve a precise output, high-resolution features from the encoder path are concatenated with the up-sampled output. Consequently, pixel-wise predictions can be made.  
As with many deep learning models, U-Net is susceptible to vanishing gradient issues. Although deepening the networks is intended to extract the complex information that cannot be obtained in the background, it also tends to reveal the problem of vanishing gradients. The reason for this is that in the back-propagation stage, the gradient of the loss values updates very little to the previous layers, or sometimes does not update at all. Additionally, as the depth of the network increases, it becomes more difficult to optimize and reaches a kind of accuracy saturation, which leads to higher training error rates [41]. Accordingly, [PERSON] et al. [1] introduced residual learning, called ResNet, which can extract underlying features by increasing the depth. Basically, it is a deep-learning algorithm used to classify images. By integrating shortcut connections into the plain network and copying identity mapping from a shallower model, residual learning will prevent the training error of a deeper model from increasing. The ResNet architecture includes residual connections, which enable the gradients to be carried forward and work out the vanishing gradient problem by going deeper. A ResNet model is identified based on the number of layers it contains. There are five different ResNet models published with 18, 34, 50, 101, and 152 layers. There is an increase in model depth from ResNet 18 to ResNet 152. In order to resolve a highly non-linear problem, important features can be extracted by deepening the model architecture. However, deeper models require high computation costs and hyperparameter tuning is more challenging compared to shallow models. By exploiting ResNet's ability to provide identity mapping between layers, these training problems can be overcome by extracting high-resolution complex features.  
[PERSON] et al. [8] introduced a novel method for segmenting roads by combining the strengths of U-Net and deep residual learning. This cooperation solves the vanishing gradient problem and performs a powerful segmentation. In the case of ResNet, the network can be trained more easily, while in the case of U-Net, the intricacies of the algorithm will be greatly reduced. As part of this approach, called deep residual U-Net architecture, residual blocks are incorporated into the encoder stages. Thus encoders of ResNet are responsible for extracting difficult-to-obtain features from images, while decoders of U-Net are responsible for generating segmentation masks with precise localization.