3D/4D Geographic Point Cloud Time Series Analysis

Surface dynamics within a local landscape occur on a large range of spatiotemporal scales. The analysis of surface activities and structural dynamics in 4D point cloud data has therefore become an integral part of Earth observation. These data contain detailed 3D information of the topography with time as additional dimension. Information derived on surface dynamics from 3D time series can provide new insights into Earth surface processes and human-environment interaction. Therefore, in this module you will learn to:

• handle 3D/4D point cloud data and perform fundamental operations
• perform point cloud analysis with Python and automate workflows
• differentiate concepts of change analysis
• apply methods for change detection and analysis in 3D point clouds
• derive change information from multitemporal point clouds and 3D time series
• analyze 4D point clouds with automatic workflows using open-source Python tools
• apply machine learning methods for 3D/4D point cloud analysis (supervised and unsupervised classification, supervised regression)
• conduct case study analyses with 4D point clouds in different use cases.

Structure

The module is structured into the following themes:

• Principles of 3D/4D geographic point clouds
• Programming for point cloud analysis with Python
• Principles and basic algorithms of 3D change detection and analysis
• Time series analysis of 3D point clouds
• Machine learning-based 3D/4D point cloud analysis
• Case study: Multitemporal 3D change analysis at an active rock glacier
• Case study: Time series-based change analysis of sandy beach dynamics

Prerequisites to perform this module

The following skills and background knowledge are required for this module.

• Basics of statistics
• Basics of geoinformation systems and handling raster/vector data
• Principles of remote sensing
• Basic programming skills (Python will be used here)

Follow this link for an overview of the listed prerequisites and recommendations on external material for preparation.

Software

For this module, you will need the software listed below. Follow the links to the individual software or tools, for help in setting them up.

• CloudCompare for point cloud visualization and editing
• QGIS for visualization and editing of results (e.g., Digital Terrain Models)
• Python for programming of point cloud analysis

Use Cases and Data

In the research-oriented case studies, this module uses multitemporal 3D point clouds of an active rock glacier and 4D point clouds of a sandy beach.

Start the module

... by proceeding to the first theme on Principles of 3D/4D geographic point clouds.