Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications.

Area of Science:

• Geosciences
• Remote Sensing
• Geotechnical Engineering

Background:

• Landslides pose significant risks to urban areas and infrastructure, necessitating regular monitoring.
• Traditional landslide monitoring methods are often inefficient, costly, and data-limited.
• Automated remote sensing offers a promising alternative for landslide progression evaluation.

Purpose of the Study:

• To develop an automated methodology for assessing landslide changes using unmanned aerial vehicle (UAV) image-based point clouds.
• To enable the calculation of horizontal displacement rates from landslide scarps across multiple time periods.
• To provide an efficient and accessible approach for landslide progression evaluation.

Main Methods:

• Development of an automated change detection methodology using low-altitude UAV image-based point clouds.
• Extraction of landslide scarps from point clouds acquired over different time periods.
• Comparison of scarps to derive horizontal displacement rates, utilizing automated geometric measurements.

Main Results:

• The developed method achieves fast processing by leveraging automated geometric measurements.
• The approach successfully processes numerous images from various epochs to create registered point clouds.
• It minimizes the need for extensive ground control points or additional processing like image correlation.

Conclusions:

• The proposed automated methodology offers a promising advancement in landslide research and monitoring.
• This technique provides an efficient and accessible tool for evaluating landslide progression.
• The results support the use of UAV-based remote sensing for regular landslide change detection.