# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 1 Introduction
Due to the repeated implementation of max-pooling operation in the encoder part, these models suffer from a severe loss of spatial information. In the dilated (also known as \"atrous\") convolution models, dilated convolutional layers are utilized instead of using the max-pooling operation to address the spatial information loss problem. DeepLab families [14] are some of the most popular models that incorporate dilated convolutions in their architectures. Specifically, the DeepLabV3 model [15] by [PERSON] et al. is based on three key hallmarks: first is the application of atrous convolution layers, second is the implementation of Atrous Spatial Pyramid Pooling (ASPP) that enables the input image at multiple scales, and, finally, the last is the combination of CNN methods such as VGG16 or Inception models with probabilistic graphical models to capture object boundaries. Meanwhile, in the realm of medical image processing, a U-Net model [16] by [PERSON] et al. was proposed to segment biological microscopy images. The U-shaped model adopting an encoder-decoder structure consists of four encoding and four decoding stages and a bridge block that connects them. One of the salient features of the U-Net model is the introduction of skip connections that enable the model to retrieve any spatial information lost during pooling operations in the encoder. Since then, there have been numerous variations of the model for tackling different domain segmentation tasks [17; 18; 19; 20; 21]. In the area of deep learning-based road extraction from UAV imagery, one of the pioneering studies to utilize Deep neural networks was carried out by [PERSON] et al. [22]. They proposed a model based on restricted Boltzmann machines (RBMs) [23] to extract road information from UAV imagery. Their method included preprocessing and postprocessingsteps. Preprocessing was applied to reduce the high dimensionality of high resolution of input images and postprocessing was deployed to polish up the disconnected parts of the predicted roads. The method proposed by [24] was fully based on Convolutional Neural Networks (CNNs) and the authors used model averaging displacement method to overcome overfitting during the inference time. Inspired by successful implementation of U-Net model into medical image processing, [PERSON] et al. proposed Residual U-Net [17] for extracting road information from remote sensing images. The authors injected the property of residual units within encoder-decoder stages into the classic U-net architecture to improve performance. Furthermore, [PERSON] et al. proposed a network based on ResNet-34 pretrained on ImageNet-1000 dataset and a decoder adapted from vanilla U-Net model [25]. In addition, to improve performance, the authors used test-time augmentation techniques. [PERSON] et al. introduced CasNet, which consists of two networks: one for road detection task and the other is for obtaining centerline detection [3]. During the inference time, a thinning algorithm was proposed to obtain smooth and complete road centerline. RoadNet proposed by [PERSON] et al. simultaneously deals with three tasks: road surface segmentation, road edge detection, and road centerline extraction [26]. Although most of the aforementioned deep learning-based models for the road extraction from the UAV imagery perform well due to their high computational complexities, these models are still too heavy to be implemented or deployed in practice on resource-constrained UAVs. The computing capacities of edge modules installed on UAVs are considered weaker than the computational capability of most deep learning platforms. AI edge modules, such as Nvidia Jetson TX2 and Jetson Nano, are some of the modules used for drones, and their computational capability is constrained compared to GPU machines used for deep learning platforms. The heaviness or lightness of networks refer to the number of parameters and the number of floating point operations of the network.