Rivers’ Water Level Assessment Using UAV Photogrammetry and RANSAC Method and the Analysis of Sensitivity to Uncertainty Sources

Abstract

Water-level monitoring systems are fundamental for flood warnings, disaster risk assessment and the periodical analysis of the state of reservoirs. Many advantages can be obtained by performing such investigations without the need for field measurements. In this paper, a specific method for the evaluation of the water level was developed using photogrammetry that is derived from images that were recorded by unmanned aerial vehicles (UAVs). A dense point cloud was retrieved and the plane that better fits the river water surface was found by the use of the random sample consensus (RANSAC) method. A reference point of a known altitude within the image was then exploited in order to compute the distance between it and the fitted plane, in order to monitor the altitude of the free surface of the river. This paper further aims to perform a critical analysis of the sensitivity of these photogrammetric techniques for river water level determination, starting from the effects that are highlighted by the state of the art, such as random noise that is related to the image data quality, reflections and process parameters. In this work, the influences of the plane depth and number of iterations have been investigated, showing that in correspondence to the optimal plane depth (0.5 m) the error is not affected by the number of iterations.

On-Site Stability Assessment of Rubble Mound Breakwaters Using Unmanned Aerial Vehicle-Based Photogrammetry and Random Sample Consensus

“…Besides its enhanced computational efficiency, RANSAC presents another important advantage in its scalability concerning the size of the input PC and the number and size of the shapes within the data. This algorithm has already been validated in other applications, such as the automatic extraction of building elements (e.g., roof planes and walls) [46,47], structural planes of rocky slopes [48], water-level planes [49], etc. In these studies, the application of RANSAC has demonstrated efficient performance, even in photogrammetric PCs, which are typically noisier than those obtained through TLS or LiDAR.…”

Dimensional measurement of anti-stress cone of HV cable joints based on 3D point cloud

“…The circle fitting methods commonly used include mean method [17], RANSAC method [18], least squares method [19], etc. Since the mean and least squares methods are sensitive to interference points, the RANSAC algorithm has the strongest resistance to interference, so the RANSAC algorithm is chosen to obtain the fitted circle.…”

RANSAC for Robotic Applications: A Survey

“…An example of simple shape detection in nature can be found in [ 91 ], where the authors develop a method for monitoring the water level in a river for a flood warning system. A UAV records aerial images, and a dense point cloud is obtained from them using photogrammetric software.…”

“…Open3D is the library behind indoor3D, the library for processing 3D data from indoor scenes [ 109 ]. Another Python library not mentioned so far is pyRANSAC-3D [ 129 ], employed, for example, in [ 91 ]. The characteristics of these libraries, along with those previously presented, are summarized in Table 5 .…”