EGU24-5670, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Enhancing 3D Feature-based Landslide Monitoring Efficiency by Integrating Contour Lines in Laser Scanner Point Clouds

Kourosh Hosseini1, Jakob Hummelsberger1, Daniel Czerwonka-Schröder2,3, and Christoph Holst1
Kourosh Hosseini et al.
  • 1Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany (
  • 2Department of Geodesy, Bochum University of Applied Sciences, Bochum, Germany
  • 3Geo Field Services and Data Management, DMT GmbH & Co. KG, Essen, Germany.

Landslides are a pervasive natural hazard with significant societal and environmental impacts. In addressing the critical need for accurate landslide detection and monitoring, our previous research introduced a feature-based monitoring method enhanced by histogram analyses, straddling a middle ground between point-based and point cloud-based methods. This paper expands upon that foundation, introducing an innovative contour line extraction technique from various epochs to precisely identify areas prone to deformation. This refined focus diverges from conventional methodologies that analyze entire point clouds. By applying on regions where contour lines do not match, indicating potential ground movement, we significantly elevate the efficiency and precision of our feature-based monitoring system.


One of the principal challenges of feature-based monitoring is managing a substantial number of outliers. Our prior research tackled this issue effectively by integrating feature tracking with histogram analysis, thereby filtering these outliers from the final results. However, the process of extracting features from each patch and matching them with corresponding patches from different epochs was time-intensive.


The incorporation of contour line extraction into our workflow, using high-resolution laser scanner data, allows for a more focused and efficient analysis. We can now identify and analyze areas of landscape alteration with greater accuracy. This approach limits the application of feature tracking and histogram analysis to these critical areas, thus streamlining the process and significantly reducing computational demands. This focused methodology not only accelerates data processing but also enhances the accuracy of landslide predictions.


Our findings indicate a substantial improvement in the efficiency of landslide monitoring methods. This methodology represents a promising advancement in geospatial analysis, particularly for environmental monitoring and risk management in regions susceptible to landslides. This research contributes to the ongoing efforts to develop more effective, efficient, and accurate approaches to landslide monitoring, ultimately aiding in better informed and timely decision-making processes for hazard mitigation and risk management.

How to cite: Hosseini, K., Hummelsberger, J., Czerwonka-Schröder, D., and Holst, C.: Enhancing 3D Feature-based Landslide Monitoring Efficiency by Integrating Contour Lines in Laser Scanner Point Clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5670,, 2024.