# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 1 Introduction  
With the acceleration of urban and rural construction, quickly identifying and extracting roads and updating road networks has become a crucial issue [1; 2; 3; 4]. As a significant component of urban transportation, roads play an essential role in political [5; 6; 7], economic [5; 8], and military fields [9; 10], among others. At present, with the development of high-spatial-resolution remote sensors, the spatial resolution of remote-sensing images can be as fine as the submeter level [11]. Unlike the thin line shape that roads present in low-resolution images, roads in high-resolution images are continuous homogeneous regions, which means that roads can be extracted from these images more accurately [10; 12]. However, due to the influence of 'different objects with similar spectra', different image resolutions, different road types, road occlusions, etc., the difficulty of designing road extraction algorithms is also increasing [13; 14]. Automatic road extraction methods represented by deep learning have been widely reported in previous studies [1; 15; 16; 17]. For example, [PERSON] et al. extracted the roads from optical satellite images using a refined deep residual convolutional neural network with a post-processing stage [2]. [PERSON] et al. proposed using recurrent convolution neural network U-Net to extract roads and predict center lines [3]. [PERSON] et al. applied a fully convolutional network (FCN) that introduced a weighted loss function to extract roads from aerial images [18]. However, they all need a sufficient number of representative training data andthe prediction ability is highly related to the training samples fed into the model [3, 19, 20]. Owing to the complexity of roads themselves, automatic road extraction models cannot achieve good results through the direct application on another dataset [21, 22, 23, 24]. Therefore, the limited remote-sensing datasets with labels are an obstacle to developing and evaluating new deep learning methods [19, 25]. In actual road labeling, road networks are still extracted by traditional manual sketching [26]. Although manual operation ensures that the topological relationship is established accurately, the workload is large and the efficiency is low [27]. It is necessary to propose a semi-automatic method that can improve the efficiency of road sample labeling [3]. Considering the need for deep learning methods and to improve the labeling efficiency to build new road datasets, semi-automatic road extraction methods combined with computer visual interpretation are still the focus of current research [9, 13, 22]. In accordance with the process and focus of the extraction algorithm, existing semi-automatic road extraction methods are mainly based on regional growth [28, 29, 30]; dynamic programming [31, 32, 33]; edge detection [34], including contour identification by finding the gradient and potential of the image [35] followed by edge thinning and division [36]; image segmentation [9, 10, 23]; template matching [37, 38]; active contour models such as Snake [39, 40, 41]; and machine learning and neural networks [2, 21, 37, 42]. However, low efficiency and poor robustness remain as problems. The approaches of dynamic programming and Snake have complicated manual settings and are subject to the time-consuming process of iterative optimization [31, 39]. Algorithms based on template matching and neural networks are greatly influenced by the template and the sample of the label. Hence, the adaptability of these models to different roads is not reliable [37]. Numerous road extraction methods based on regional growth, edges, and image segmentation have been developed, but they may confuse roads with surrounding objects due to the complexity of road edges [43]. At the same time, most of these semi-automatic methods focus on image raster processing, and there is little consideration of centerline extraction, vectorization, and road network generation.