# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
The ANN consists of neurodes that are arranged in layers, including the input layer, hidden layers and output layer. The backpropagation algorithm is employed in the training process. In the study, the authors used Ikonos images, and 10 neurodes were assigned to the hidden layer in their proposed model. However, the calculation of this model is slow. It requires more samples for training, and it easily falls into the local minima. If the number of classes increases, the model provides inadequate accuracy because of overfitting [1]. To overcome the aforementioned problems, [PERSON] et al [21] provided a breakthrough: his representation of convolutional neural networks (AlexNet) included five convolutional layers and three fully connected layers and has been generally used to advance many topics in deep learning. One of these topics is the use of remote sensing to extract objects including buildings, roads, etc. AlexNet was trained for the classification task by using high resolution images for the ImageNet LSVRC-2010 contest. They used data augmentation to avoid overfitting, and their work confirmed that more convolution layers increased the performance. After that, many studies have been conducted using CNNs to extract roads from aerial images. One of the first and most well-known works using the basic architecture of convolutional neural networks (CNNs) to detect roads from aerial images is the study by [PERSON] et al. [22]. These authors used a patch-based approach that uses smaller patches of images, and they confirm that they need a large dataset to achieve better performance. They applied preprocessing to reduce the dimensionality of the input data, and these authors also show the importance of adding postprocessing to the CNN architecture to obtain better performance. [PERSON] et al [23] follow [PERSON]'s patch-based approach with larger patch sizes and postprocessing by using a support vector machine classifier. Their CNN consists of four convolutional layers and two fully connected layers, but their network has worse performance for the images that contain diagonal roads. [PERSON] et al. [24] created a segmentation model by stacking 11 layers in a convolutional neural network. They used polarimetric SAR images that included L-band data from over San Francisco bay area and C-band data for Flevoland. They found that the average and max pooling procedures provide similar performance. The basic design of the CNN also has been used in classification problems [25], and their classification model consists of seven convolutional layers and global average pooling. Their model succeeded in classifying the images into four teeth categories. Neural networks have undergone great enhancements and improvements to their design architecture to address different problems. One of these problems is training a deep convolutional neural network, which includes more stacked layers than the previous traditional CNN. When training these deep neural networks, the convergence will be inhibited due to the exploding or vanishing gradients [26; 27]. Two such improvements are deep residual blocks and identity mapping [26; 27]. These improvements ease the training of deep neural networks by preventing the gradients from vanishing or exploding, which inhibit the convergence of the networks. [PERSON] et al. [28] present a new evolution architecture for the CNN that replaces the fully connected layers with convolution layers (Conv. layer). This special CNN is referred to as a fully convolutional network (FCN). The goal of their network is to produce an output image size similar to the input ones with adequate learning and inference. They use the architectures of AlexNet [21], VGG Net [29] and GoogleNet [30] and convert the method into an FCN. Many improvements and new designs for neural networks based on the FCN design concept have appeared. U-Net [31] is considered to be a revolutionary design for dealing with semantic segmentation tasks, particularly in medical imagery.