# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
U-Net uses a relatively small number of datasets that are preprocessed by using an intensive data augmentation procedure; it is known as a symmetric architecture with a contracting or encoding part that can deal with the context and an expanding or decoding part to deal with object localization. SegNet [32] represents a deep fully convolutional neural network with a basic auto-encoder design that followed the VGGNet design [29] and has 13 convolutional layers. The decoder part is responsible for enhancing the resolution of the low feature map to prepare it for the semantic segmentation task. SegNet also provides good results for semantic segmentation problems. [PERSON] et al. [33] present a semantic segmentation model for road extraction from aerial images. This model combined U-net with three ResBlocks for both contracting and expanding paths. They use only a cropping technique on the dataset to get smaller image sizes without using any further data augmentation, and their network provides good segmentation results for road extraction problems. [PERSON] et al. [34] also focus on road extraction from aerial images using a segmentation model combining U-net with a Residual neural network (ResNet-34 pretrained on ImageNet) on the contracting path with a vanilla U-net on the expanding path. They applied data augmentation on their dataset, and they emphasized the importance of preparing high quality labeled masks in order to achieve good predictions. [PERSON] et al. [35] extracted roads from high-resolution aerial imagery with global and local information using DenseNet. Based on the symmetric design of U-Net, their architecture consists of two parts: one is the encoding part based on a pretrained DenseNet model, and the other is the decoding part that creates the classification map. They also design the local and global attention units in the decoding part to enhance the predictions. As mentioned above, the design of CNNs went through many phases. In the first phase, the ANN faced overfitting problems and produced less accurate results if three or more classes were considered. In the design of the first architecture of the CNN with convolution layers, the disadvantage was that it required using many preprocessing and postprocessing techniques in order to improve the predictions. In the current phase, the DCNN overcomes many obstacles that the traditional CNN faced, such as the training problems for deep layers. The DCNN produces better predictions with less preprocessing and it requires almost no postprocessing. As a part of the DCNN family, the U-net architecture has been well researched in combination with different types of convolutional neural networks. U-net provides good results, especially in semantic segmentation problems and especially in comparison with other architectures. Therefore, a DCNN will be used in this study to automatically and efficiently extract the road networks. Our model combines U-net as a basic architecture with ResBlocks in the encoding part and simple Conv. layers in the decoding part. The aim of this architecture is to design an efficient neural network that can simply and efficiently deal with semantic segmentation problems using small amounts of data. Currently, many DCNN architectures have achieved outstanding results, especially in semantic segmentation tasks, compared with the state-of-the-art models, but they also have very expensive computational costs because of their complicated designs. our proposed model is lighter, less complicated to implement in any machine, and generates good predictions.