Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions.

This paper presents an innovative computer graphic method for viewshed generation from big point clouds. The proposed approach consists in simplification of typical methods for viewshed formation that are based on sorting and binary trees. The proposed method is based on the k-d tree concept optimized with radial segmentation and a dedicated mathematical algorithm for subtree rejection. The final visualization of the viewshed is designed with a graphic method using triangulated irregular network (TIN) surfaces from the accepted subtrees.

In this paper we have proposed a method of solving the computer graphic problem of creating a Triangulated Irregular Network (TIN) surface in large clouds in order to create viewsheds. The method is based on radial TIN surface and viewshed visualization task subdivision using multiple computing machines, which is intended to accelerate the process of generating the complete viewshed.

Point cloud based viewshed generation in AutoCad Civil 3D