Evaluating the ground point classification performance of Agisoft Metashape Software

Abstract

This paper investigates the complex process of extracting bare land surfaces from point clouds, with a particular focus on filtering out objects such as trees, buildings, and vehicles. It underscores the importance of this task in diverse domains, including cadastral surveying, base mapping, and various geographical sciences, all while excluding specific reference to LiDAR and GIS applications. The research provides an extensive exploration of different algorithms used for point cloud filtering, culminating in a comprehensive evaluation of Agisoft's ground point filtering algorithm in contrast to the well-recognized CSF method. For this comparison, an Unmanned Aerial Vehicle (UAV) flight was performed at Harran University's Osmanbey campus to generate the necessary point cloud. The results of this assessment reveal that a significant portion of the obtained points pertains to ground points, underscoring the efficacy of the filtering process in producing Digital Terrain Models (DTMs). The numerical findings demonstrate that the overall accuracy stands at 0.002, with minimal Type I and Type II errors, reaffirming the robust performance of the filtering algorithms in producing accurate DTMs.