Recently, the use of unmanned aerial vehicles (UAVs) has increased in popularity across several industries. Most notable, however, is the impact that this technology has had in research at academic institutions worldwide. As the technology for UAVs has improved, with that comes easier to operate, more accessible equipment. UAVs have been used in various types of applications and are quickly becoming a preferred method of studying and analyzing a site. Currently, the most common use of a UAV is to monitor a location of interest to a researcher that is difficult to gain access to otherwise. The UAV can be altered to meet the needs of any given project and this versatility has contributed to their popularity. Often, they are equipped with a type of remote sensor that can gather information in the form of images, sounds, heat, or light. Once data has been gathered from a site, it is processed and modified, allowing it to be studied and analyzed. A process known as Structure from Motion (SfM) creates a 3D digital terrain model from camera images captured through the use of a UAV. SfM is a common method of processing the vast amount of images that are taken at a site and the 3D model that it creates is a helpful resource for analysis. These digital models, while useful, are oftentimes created at an unknown accuracy. This research presents a comparative study of the accuracies obtained when different parameters are applied during the SfM process. The results present a comparison of the time required to process a particular model and the accuracy that the model had. Depending on the application and type of project, a desired level of accuracy can be obtained in the presented amount of time. This particular study used a landslide as the site of interest and captured the imagery using a helicopter UAV.



College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering



Date Submitted


Document Type





UAVs, remote sensing, structure from motion, landslide monitoring, computer vision