Heidelberg University

2019 - 2022


Research Project

GEODYNAMO4D – Tracing geographic dynamics on 4D Point Clouds


Research news can be found in our GIScience News Blog and on Twitter: #GEODYNAMO4D.


The Earth’s surface is subject to changes at a range of temporal and spatial scales. Using remote sensing methods, such as laser scanning and dense image matching, we can create three-dimensional point clouds of this surface. By analysing point clouds acquired at different points in time, we want to develop our understanding of the various geomorphic processes that contribute to this change. While methods to quantify change between two 3D point clouds have been presented, they often do not take advantage of knowledge about the data itself, about the method of its acquisition, and the monitored object. Use of this additional information is anticipated to enable an improved quantification of change itself, but also of the estimation of significance, effectively lowering the level at which change can be detected.


Left: Change between 2015 and 2018 on the edge of the Äußeres Hochebenkar rock glacier, Austria. Right: Estimated uncertainty of the quantified change based on error propagation of sensor- and co-registration errors.


The GEODYNAMO4D research project develops methods for the analysis of time series of point cloud data, referred to as 4D point clouds. The goal therein is robust quantification of geometric change, tracing of the change direction, and the identification of areas where we cannot reliably determine change. Identifying areas with similar spatiotemporal change patterns will allow the separation of superimposed processes working in geometrically similar, yet distinguishable ways; for example, in separating creep of a rock glacier from rolling/sliding events of individual boulders.

The task will be approached on three different levels:

  1. Incorporating knowledge on the sensing process, i.e. measurement errors, using error propagation.
  2. Using the full time series to extract patterns that are otherwise attributed to noise by applying signal processing methods and machine learning.
  3. The observed objects and their changes are not arbitrary, but rather follow certain laws, that can be understood as system constraints. These constraints can e.g. be enforced in a neural network, increasing training speed and reducing network complexity.



The developed methods should be very general and applicable to many tasks concerned with time series of topographic point cloud data. This includes all processes that have a temporal and a spatial component and specific characteristics in their change over time, for example geomorphological processes and vegetation dynamics (cf. Project SYSSIFOSS).

Virtual laser scanning (HELIOS) will provide additional simulated measurements that can be used as datasets with exactly-known ground truth. Combined with simulation of geomorphological processes, this allows easy testing of the developed algorithms over multiple time scales and supplies practically unlimited training data for machine learning applications.

Related Projects
  • HELIOS: Heidelberg LiDAR Operations Simulator.
  • Auto3DScapes: Autonomous 3D Earth observation.
  • SYSSIFOSS: Synthetic structural remote sensing data for improved forest inventory models.
  • Geomorph4D: Characterising multi-process geomorphic change.
  • AHK-4D: High-resolution and high-frequency monitoring of the rock glacier Äußeres Hochebenkar (AHK) in Austria.
Related 3DGeo Publications
  • Anders, K., Winiwarter, L., Lindenbergh, R., Williams, J.G., Vos, S.E. & Höfle, B. (2020): 4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 159, pp. 352-363. DOI: https://doi.org/10.1016/j.isprsjprs.2019.11.025.
  • Anders, K., Lindenbergh, R. C., Vos, S. E., Mara, H., de Vries, S., and Höfle, B. (2019). High-frequency 3D geomorphic observation using hourly terrestrial laser scanning data of a sandy beach, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 317-324. DOI: https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019.
  • Eitel, J.U.H., Höfle, B., Vierling, L.A., Abellán, A., Asner, G.P., Deems, J.S., Glennie, C.L., Joerg, P.C., LeWinter, A.L., Magney, T.S., Mandlburger, G., Morton, D.C., Müller, J., Vierling, K.T., 2016. Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences. Remote Sensing of Environment. Vol. 186, 372–392. DOI: https://doi.org/10.1016/j.rse.2016.08.018.
  • Winiwarter, L., Mandlburger, G., Schmohl, S., Pfeifer, N. (2019): Classification of ALS Point Clouds Using End-to-End Deep Learning. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. Vol. 2019 (3), pp. 75-90. DOI: https://doi.org/10.1007/s41064-019-00073-0.
  • Zahs, V., Hämmerle, M., Anders, K., Hecht, S., Rutzinger, M., Sailer, R., Williams, J.G., Höfle, B. (2019): Multi-temporal 3D point cloud-based quantification and analysis of geomorphological activity at an alpine rock glacier using airborne and terrestrial LiDAR. Permafrost and Periglacial Processes. Vol. 30 (3), pp. 222-238. DOI: https://doi.org/10.1002/ppp.2004.
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Latest Revision: 2020-01-13
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