py4dgeo

Open-source scientific software for topographic change analysis in 3D/4D geographic point clouds

Journal Article (2026)
Author(s)

K. Anders (Technische Universität München)

D. Kempf (Universität Heidelberg)

W. Albert (Universität Heidelberg)

P. Andriushchenko (Heidelberg University Hospital, Universität Heidelberg)

X. Huang (Technische Universität München)

D. Hulskemper (TU Delft - Civil Engineering & Geosciences)

T. Isensee (Universität Heidelberg)

D. Kapitan (Universität Heidelberg)

R. Tabernig (Universität Heidelberg)

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Research Group
Optical and Laser Remote Sensing
DOI related publication
https://doi.org/10.1016/j.softx.2026.102670 Final published version
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Publication Year
2026
Language
English
Research Group
Optical and Laser Remote Sensing
Journal title
SoftwareX
Volume number
34
Article number
102670
Downloads counter
8
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Abstract

The software py4dgeo is an open-source Python library for automated analysis of 3D/4D geographic point clouds with a focus on topographic change detection and quantification, and on surface dynamics analysis. py4dgeo addresses a growing need for robust, reproducible, and extensible tools capable of handling complex spatiotemporal datasets, especially for geographic applications and topographic monitoring in general. The library implements state-of-the-art methods for point cloud registration, bitemporal 3D change analysis, and time series-based approaches. py4dgeo features a modular architecture combining a C++ core designed for computationally demanding tasks with a flexible Python interface, enabling robust processing of large datasets while supporting rapid scientific method development. Its object-oriented data structures manage temporal point cloud series, core points, and spatiotemporal neighborhoods in a transparent and extensible way. Full interoperability with widely used tools such as PDAL, CloudCompare, and the Python ecosystem (e.g., via LAS/LAZ format and NumPy arrays) facilitates seamless integration into existing workflows. The library is accompanied by comprehensive documentation, open data-based Jupyter demos, and community-driven development to support reproducibility and encourage contributions. py4dgeo provides a scalable foundation for monitoring dynamic 3D environments and is already used in numerous research and application projects for automated, quantitative change analysis in 4D point clouds. Therefore, py4dgeo contributes an essential resource to the growing field of spatiotemporal analysis of geographic point cloud data.