In recent years, the advent of unmanned aerial vehicles (UAV)-based photogrammetry has enabled the collection of accurate and comprehensive information from the surface of the Earth. Owing to low-altitude flights, it is possible to generate high-density point clouds, which are useful for accurate representation of topography of the land surface. Ground filtering is the removal of the points belonging to above-ground objects in order to retrieve ground points to be used in generating digital terrain models. It is essential in most applications for modelling the environment and is performed by using various types of commercial and non-commercial software. This study investigates the performances of seven widely used ground filtering algorithms on UAV-based point clouds. These algorithms are (1) the adaptive triangulated irregular network implemented into the commercial Agisoft Photoscan Professional software, (2) the multi-scale curvature classification implemented into the commercial global mapper software, (3) the cloth simulation filtering (CSF) applied with a MATLAB script, (4) the interpolation-based Boise Centre Aerospace Laboratory-lidar algorithm embedded in the commercial environment for visualizing images software, (5) the interpolation-based gLiDAR non-commercial software, (6) the 2D progressive morphological algorithms, and (7) elevation threshold with expand window algorithms embedded in the non-commercial airborne lidar data processing and analysis tools software. The results showed that the CSF algorithm presented the best filtering results for both test sites with Total Errors of 6% and 4.5% in the sites 1 and 2, respectively.