Clean orthophotos and 3D Mesh - Point clouds serve as visual data that not only enable clear communication during meetings but also deliver excellent results in terms of surveying accuracy. Today, we will introduce how to obtain the high-quality 3D point clouds that form the foundation of drone surveying analysis.
First, you should fly the drone in a good shooting environment. Drone surveying can also be described as photogrammetry. The principle of photogrammetry is to extract feature points based on the overlap of photos taken from multiple angles and build a three-dimensional model. Therefore, naturally, the clearer the information obtainable from the captured photos, the higher the accuracy of the results.
The more the information of the area you want to survey is obscured—such as terrain densely covered with trees and vegetation, or terrain with many shadows—the higher the probability that errors will occur in the analysis results. Likewise, areas with many reflective surfaces, such as snow-covered ground, lakes, or puddles, can also produce errors for the same reason.
Therefore, it can be a good approach to capture cleared terrain on a clear day, during a time of day when shadows are minimal.
You may have experienced a subject coming out shaky and stretched when capturing a panorama or photo while moving the camera. Drones, too (although this varies by model), produce a distortion phenomenon in photos taken during high-speed flight. This is called the jello effect. As explained earlier, if the captured photos contain noise, blur, or distortion, errors can occur in the data analysis.
There are several ways to solve this kind of problem. The first is a hardware approach: switching the drone to one with a camera sensor or a mechanical shutter. The second is a software approach: analyzing the data by leveraging the metadata acquired during drone shooting and correcting displacement values according to the camera specifications.
Meissa can help with the latter approach. Meissa has its own drone analysis engine, enabling flexible solutions tailored to the data acquired by the drone. You can find more details about this in this article.
Lowering the flight altitude can also be one approach, because it is a way to improve the GSD.
GSD (Ground Sample Distance) means ground sampling distance. Here, the distance is the distance between the center points of two consecutive pixels. For example, if there is an orthophoto with a GSD of 1cm/px, one pixel of this orthophoto actually represents a size of 1cm. The smaller the GSD, the more precisely the details are revealed in the image.
Unlike pixel resolution, GSD varies depending on the distance between the camera and the subject. GSD improves as the camera gets closer to the object.
Just as we prefer maps with a small scale for wayfinding, a small GSD interval is also preferred for accurately calculating volumes during surveying. In a previous article, we introduced a surveying experiment conducted with a general-purpose rotary-wing aircraft at an actual DL E&C site. As a result, for the Z coordinate, we were able to confirm that the lower the flight altitude, the smaller the mean error and standard error. This means that the lower the flight altitude, the higher the surveying accuracy.
Of course, if you photograph a large area at a low altitude, the number of photos the drone takes and the time required increase dramatically. Drone batteries at a price that is not burdensome for field use last about 20-25 minutes, and it can be difficult to obtain the desired data within that time.
At Meissa, we set the drone flight path by focusing on the best altitude and overlap achievable within the limited time. After data analysis, the GSD is fixed at 5cm/px, which is sufficient for practical use. However, you may want a narrower GSD for report documentation or other reasons. In that case, denser modeling is possible through a separate analysis via a technical support request.
