###### Abstract  
The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we first extract the boundary of the cliff to segment the point cloud according to a designed rule, then calculate the centroid coordinates of each point cloud block, followed by the coordinates of side points on both sides of the boundary of each block from the centroid, and finally create a side point cloud projected to the water surface corrected for elevation. The corrected point cloud is considered a point cloud dataset of the innermost  
a digital elevation model (DEM) and then processed the DEM to extract the shoreline. [PERSON] et al. [5] explored the adaptability of an algorithm to various coastal zone data types, and proposed a specific method with high accuracy and adaptability for shoreline extraction in shoals and muddy areas. Based on Monte Carlo simulations, [PERSON] et al. [6] extracted the shoreline using LiDAR data and evaluated its accuracy; the method generates a DEM from LiDAR data, intersects the DEM with the tidal datum, and then separates land and water images. After image processing, mathematical morphology operation, line tracking, and vectorisation, the shoreline data are finally obtained. Furthermore, [PERSON] et al. [7] used the intensity information in airborne LiDAR data to extract the shoreline; the method first converts the intensity information in the point cloud into an infrared band, and then extracts the shoreline after a certain method design and process. [PERSON] et al. [8] attempted to separate the land and water points in LiDAR data to extract the shoreline; they used terrain, slope, and point density information from the obtained data and combined it with the sample information of land and water. Similarly, [PERSON] et al. [9] attempted to separate water and land using airborne LiDAR data and by combining the characteristics of ground features. Separating land and water points can provide a better reference for shoreline extraction. In some studies, LiDAR data were combined with image data to extract shorelines. For instance, [PERSON] et al. [10] combined point cloud and image data, detected and removed outliers through morphological methods, and removed artificial facilities, such as ports, outside the shoreline through Hough transformation; this method requires DEM from point cloud data. [PERSON] et al. [11] combined point clouds and images to extract the boundaries of a lake. [PERSON] et al. [12] extracted the shoreline through the fusion of hyperspectral images and generated a DEM based on LiDAR data. First, a preliminary solution was obtained, and then the shoreline was extracted using contour evolution technology. In addition, some studies [13; 14; 15; 16] have used airborne LiDAR data to estimate and analyze the location of the shoreline and then evaluated other marine elements, such as coastal protection and wind force. Although these studies did not use airborne LiDAR data to extract the shoreline, they used the shoreline obtained from an LiDAR data source to examine other marine elements. From these reports, airborne LiDAR data play an important role in shoreline extraction and other aspects of oceanic research. From the above literature, a majority of the shoreline extraction methods have used point cloud data to first generate a DEM; however, DEM construction is a very time-consuming and error-prone process, and the accuracy of shoreline extraction through DEM is low [17; 18; 19]. Moreover, when data distribution characteristics do not match, the algorithm fails. Therefore, many effective and reliable extraction algorithms suitable for specific shoreline types have been proposed through analyses of data characteristics of each type. [PERSON] et al. [20] proposed the use of deep learning and object-oriented analysis for bedrock shoreline extraction. [PERSON] et al.