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Welcome to the course "Open-source Scientific Software py4dgeo for Change Analysis in 3D/4D Point Clouds"

License: MIT

Overview

This tutorial introduces py4dgeo, an open-source Python library for analyzing geometric change and surface dynamics in 3D and 4D point cloud data. Designed to support scientific workflows, py4dgeo offers a reproducible and scalable tool for quantifying surface change in multitemporal point clouds and 3D time series across a broad range of topographic monitoring applications.

Participants will gain a solid understanding of the key concepts and challenges in 3D/4D change analysis. This includes the full pipeline of multi-temporal point cloud alignment, 3D change quantification, and time series-based quantification methods.

What to expect

The tutorial will demonstrate how py4dgeo implements state-of-the-art algorithms to address these challenges. It will emphasize the library's modular framework, offering accessible and configurable methods that empower users to perform transparent, fully automated scientific analysis. This tutorial is intended for researchers, students, and practitioners working with time-dependent 3D data who require a flexible, scalable, and open-source framework for surface change analysis.

Hands-on exercises will guide participants through practical use of the library, including:

  • Loading point cloud data,

  • Applying 3D change detection algorithms (e.g., M3C2),

  • Using a hierarchical approach for 3D change analysis,

  • Performing time series-based analysis (e.g., 4D objects-by-change), and

  • Visualizing results.

Example workflows will demonstrate how py4dgeo integrates with standard Python-based environments and complements other open-source tools such as CloudCompare.

By the end of the tutorial, participants will understand core methods for 3D/4D change analysis, be able to reproduce and adapt workflows for research or applied monitoring tasks and apply py4dgeo to their own datasets.

Software installation and data downloads

Before we can start with the course program, make sure you have the required software installed and data downloaded. The steps for installation of necessary software and download of scripts and data are documented on the Installation and Download page.

Course program

The course combines short presentations and guided practical work in notebooks. There are three activities related to change detection in 3D point cloud time series.

Activity 1: Concepts and Challenges in 3D/4D Change Detection

by Prof. Bernhard Höfle and Prof. Katharina Anders

45 minutes, presentation and guided discussion.

  1. Overview tutorial
  2. Relevance, state-of-the-art and methods
  3. py4dgeo software structure
  4. Questions and expectations

Activity 2: Hands-on with py4dgeo for Surface Change Analysis

by Prof. Katharina Anders, Xiaoyu Huang, and Ronald Tabernig

90 minutes, interactive demo and practical exercises.

  1. Short recap of installation and environment setup
  2. Standard workflow: import, bi-temporal analysis and CloudCompare
  3. Adding co-registration and checking improvements
  4. Many point clouds from PLS: relevant and non-relevant changes, spatial sub-sampling, ScOR+SOR, VAPC and point cloud processing
  5. Full time-series workflow: clustering and 4D-OBC
  6. Q&A

Activity 3: Applied Workflow and Use Case Exploration

by Xiaoyu Huang and Ronald Tabernig

75 minutes, case study, guided exercises and open discussion.

Literature and References

Journal papers and conferences
  • Albert, W., Tabernig, R., & Höfle, B. (2025). AImon5.0 (Version 1.0.0). https://github.com/3dgeo-heidelberg/AImon.
  • Anders, K., Eberlein, S., & Höfle, B. (2022). Hourly Terrestrial Laser Scanning Point Clouds of Snow Cover in the Area of the Schneeferner, Zugspitze, Germany: PANGAEA. doi: 10.1594/PANGAEA.941550.
  • Anders, K., Lindenbergh, R. C., Vos, S. E., Mara, H., de Vries, S., & Höfle, B. (2019). High-Frequency 3D Geomorphic Observation Using Hourly Terrestrial Laser Scanning Data Of A Sandy Beach. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5, pp. 317–324. doi: 10.5194/isprs-annals-IV-2-W5-317-2019.
  • Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S. E., & Höfle, B. (2021). Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 297–308. doi: 10.1016/j.isprsjprs.2021.01.015.
  • Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), pp. 239–256. doi: 10.1109/34.121791.
  • Fey, C., & Wichmann, V. (2017). Long-range terrestrial laser scanning for geomorphological change detection in alpine terrain - handling uncertainties. Earth Surface Processes and Landforms, 42(5), pp. 789–802. doi: 10.1002/esp.4022.
  • Kuschnerus, M., Lindenbergh, R., & Vos, S. (2021). Coastal change patterns from time series clustering of permanent laser scan data. Earth Surface Dynamics, 9(1), pp. 89–103. doi: 10.5194/esurf-9-89-2021.
  • Lague, D., Brodu, N., & Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing, 82, pp. 10–26. doi: 10.1016/j.isprsjprs.2013.04.009.
  • Tabernig, R., Albert, W., Weiser, H., & Höfle, B. (2025). A hierarchical approach for near real-time 3D surface change analysis of permanent laser scanning point clouds. In: 6th Joint International Symposium on Deformation Monitoring (JISDM). doi: 10.5445/IR/1000180377.
  • Yang, Y., & Schwieger, V. (2023). Supervoxel-based targetless registration and identification of stable areas for deformed point clouds. Journal of Applied Geodesy, 17(2), pp. 161–170. doi: 10.1515/jag-2022-0031.
  • Zahs, V., Winiwarter, L., Anders, K., Williams, J. G., Rutzinger, M., & Höfle, B. (2022). Correspondence-driven plane-based M3C2 for lower uncertainty in 3D topographic change quantification. ISPRS Journal of Photogrammetry and Remote Sensing, 183, pp. 541–559. doi: 10.1016/j.isprsjprs.2021.11.018.
  • AHN - Actueel Hoogtebestand Nederland. National elevation product of the Netherlands. https://www.ahn.nl/
Software

Lecturers

The tutorial was prepared by

3DGeo logo TUM logo

Acknowledgements

  • This tutorial was prepared as part of the ISPRS 2026 Tutorial Sessions under the name "Open-source Scientific Software py4dgeo for Change Analysis in 3D/4D Point Clouds".
  • This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 535733258 (Extract4D project).