# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 1 Introduction  
Automated road extraction remains one of the important yet challenging tasks in remote sensing imagery analysis. Its massive deployment in social and economic welfare such as city planning, road navigation, arrangement of logistic hubs, and evacuation planning makes it of great significance. However, the existence of non-road objects, occlusions, and complexity of the background make it difficult to extract roads precisely. Manually labeling the roads might be one of the methods to solve the problem, but due to its repetitious and wearisome nature, one is prone to making mistakes, let alone its inefficiency. Several methods have been proposed for extracting road information from raw UAV images. These methods can be divided into three categories: road area extraction, road centerline extraction, and road edge detection. Pixel-level segmentation of roads and their surface is the main task of road area extraction while extracting the skeleton or centerline [1] of roads is the main task of the road centerline extraction. Road edge detection [2] infers extracting single-pixel width of road edges, and it is important for autonomous driving car systems. In some studies, more than one single task is addressed simultaneously [3]. Road area extraction can be undertaken by pixel-wise semantic segmentation. Traditional road extraction methods used classic machine learning and computer vision algorithms such as support vector machines (SVM) and Markov random fields (MRF). [PERSON] et al. (2003) used support vector machine (SVM) and template matching [4] to extract the true road segment and remove the false road segment. [PERSON] and [PERSON] (2020) proposed a cuckoo search optimization algorithm for extracting road regions from high resolution images using multilevel thresholding schema and SVM [5]. [PERSON] et al. (2011) came up with an adaptive non-planar road detection and tracking approach that combines a piecewise planar model and an MRF-based alternating optimization method [6]. [PERSON] et al. (2017) proposed an unsupervised method that consists of steps such as feature extraction (using Gabor or morphological filtering), graph-based image segmentation, and postprocessing [7]. These traditional methods include multiple steps [7], which is time consuming and requires individual optimization of each component of the model pipeline. Currently, deep learning models have been deployed increasingly in dense classification tasks thanks to their good performance and generalization capability. Convolutional Neural Networks (CNNs) are designed to automatically and adaptively learn spatial hierarchies of features, from low to high-level patterns [8]. Fully convolutional networks (FCN) [9] for semantic segmentation by [PERSON] et al. is one of the earliest models that include only convolutional layers to extract the segmentation map. The authors modified models such as VGG16 [10] and Inception [11] networks that performed well in the object recognition domain so that the models can accept arbitrarily sized images. The fully connected layers of the models were substituted for convolution layers that upsample the extracted feature map to the same size as the input. However, due to the jump structure of the network, the fine-grained spatial information of images is lost in the upsampling process. DeConvNet [12] by [PERSON] et al. was introduced to tackle this bottleneck by using encoder-decoder architecture. The model also adopts VGG16 network as an encoder to extract high-level feature maps. However, unlike the FCN network, the segmentation map extraction task is performed by multilayer decoder part that upsamples the output of encoder by deconvolution method (also known as transposed convolution). [PERSON] et al. proposed SegNet model [13], also based on encoder-decoder architecture. However, in SegNet model, the upsampling task in the decoder is performed by using pooling indices computed in the max-pooling operation executed in the encoder part.