## 1 Introduction
This method involves placing multiple measuring points on the dry beach to measure its top elevation, using geometric projection to determine the slope angle of the beach, and incorporating the water level in the reservoir area to calculate the length of the dry beach. [PERSON] et al. [20] used angle and laser measuring instruments to measure the dry beach slope and then determined the dry beach length by incorporating the water level in the reservoir area. [PERSON] et al. [21] proposed a laser direct measurement technique that involves scanning the dry beach surface with a laser from the starting point to the water surface. This method determines the length of the dry beach by analyzing the laser's absorption and reflection properties on the dry beach and water surfaces, in conjunction with the measuring instrument's rotational angle and geometric projections. Generally, the traditional measuring method of a dry beach is usually contact measurement. The contact method requires the installation and maintenance of measuring devices on a soft deposited beach that is difficult to access. In recent years, advancements in computers and artificial intelligence have led to the application of camera vision image-processing technologies. [PERSON] et al. [22] propose a Python-based algorithm to effectively discover camera parameters, rectify images, generate  
Figure 1: The structure of a tailings pond and dry beach. disparity maps, then use these maps to measure distance accurately. [PERSON] et al. [23] propose a distance measurement method for a single-camera condition. The purpose measure the distance of objects in front of the imaging system in the indoor environment. Camera visual image processing technology involves digitally processing and analyzing images taken by monitors, video cameras, drone cameras, etc. The method analyzes images of the dry beach area near the tailing pond to detect the waterline, determine the pixel coordinates of the water-surface boundary on the dry beach, and calculate the distance using calibrated objects or coordinates in the images. [PERSON] et al. [24] introduced a photogrammetric method to measure the dry beach length. [PERSON] et al. [25] delineated the boundary between the dry beach and water by employing contour identification, threshold segmentation, and watershed algorithms on the collected images. [PERSON] et al. [26] used images captured by a surveillance camera and trained a network model with the mask R-CNN algorithm to identify the waterline and provide its coordinates. However, camera vision image processing still faces the following challenges. On-site deployment of cameras and calibration objects and equipment is necessary, requiring labor-intensive and hazardous manual deployment. The camera and monitoring recordings are not suitable for inclement weather conditions and are restricted by unfavorable weather elements [27]. Changes in the dry beach's length caused by the continuous discharge of tailings slurry require the relocation of calibration objects and measuring equipment to prevent them from being buried and rendered inoperative [24]. These problems limit the application of this method in the measurement of dry beach length in tailing ponds. This paper suggests a method for determining the dry beach length using satellite images and deep learning algorithms to address the abovementioned issues. Firstly, the YOLOv5-seg model was established to detect the dry beach area. An edge detection algorithm was then used to extract the dry beach contour. In addition, the beach crest line was fitted by an equation. Afterwards, the distance from the dry beach demarcation line to the beach crest line was calculated to determine the dry beach length. Finally, the method was validated by a tailing pond case in China.