EGU23-16037, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Homography-based continuous bridge scour depth estimation

Ana Margarida Bento1,2, Luís Mendes3, and Rui Ferreira4
Ana Margarida Bento et al.
  • 1CIIMAR – Interdisciplinary Centre of Marine and Environmental Research, Marine Energy Group, 4450-208 Matosinhos, Portugal (
  • 2Hydraulics, Water Resources, and Environmental Division, Department of Civil Engineering, Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal (
  • 3CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal (
  • 4CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal (

Scour monitoring in experimental environments relies primarily on visual point-wise measurements that may provide less accurate estimates of scour and its effects. To fulfil these issues many studies and methods has been developed to analyse scour surfaces. Recently, the use of 3D point clouds and digital elevation models has proven to be an effective method for describing scour around bridge foundations with a high degree of accuracy. This is especially true under drained conditions. Therefore, it has become necessary to develop a system that can continuously monitor the development of scour at bridge foundations without interrupting the flow. 

Few studies have addressed continuous monitoring of the scouring process. These include: (i) photogrammetry-based methods using two cameras and algorithms for image calibration, rectification, and stereo-triangulation, and (ii) a laser-based approach using both a laser source and a camera. As a result of these studies, further researches need to be developed in order to effectively monitor scouring process by using the increasing technology of submersible cameras and underwater processing capabilities. In this study, a novel method for acquiring 2D scour profiles was developed to enable continuous monitoring of the scour phenomenon. The developed technique uses a computer vision technique, namely homography transformation, which relates the coordinates of points in one image to the coordinates of corresponding points in another image through a Python routine. This algorithm also considered the critical issues inherent in any underwater image processing technique, such as correcting for perspective, distortion, scaling, and camera lens rotation. 

In the laboratory, four cameras were used to collect synchronized underwater images of the scour holes formed and the affected surrounding areas around an oblong bridge pier model due to the local scour phenomenon. By processing each image sequence and running the Python code to measure the depth of the border line between the sand and the bridge foundation model at specific times during the scouring experiment, it was possible to obtain the evolution of the scour holes in the form of 2D bed profiles. The accuracy of the developed algorithm to study the bed morphology in the vicinity of bridge piers during the scouring process showed promising results compared to point-wise scour depth measurements.

This work was partially funded by the Portuguese Foundation for Science and Technology (FCT) through Project DikesFPro PTDC/ECI-EGC/7739/2020 and through CERIS funding UIDB/04625/2020.

How to cite: Bento, A. M., Mendes, L., and Ferreira, R.: Homography-based continuous bridge scour depth estimation, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16037,, 2023.