CharAct4D – Unravelling Landscape Dynamics via Automatic Characterization of Surface Activity using Geographic 4D Monitoring
This website can be accessed via a short URL: www.uni-heidelberg.de/charact4d.
News
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Objective
The objective of this project is to fundamentally improve the characterization of landscape dynamics using novel computer-based methods of change analysis in combination with recent strategies of geo-graphic 3D monitoring. Landscape surfaces of our Earth are highly complex in their dynamics, because pro-cesses occur on a large range of spatiotemporal scales. From high-mountain glaciers to sandy coasts, surface activity within a local landscape occurs at different magnitudes, spatial extents, velocities, and return frequencies. Capturing and characterizing surface dynamics within a landscape – both naturally or human-induced – is key to increasing our understanding of underlying environmental processes and their complex interplay with human actions. Extending our knowledge in this respect is of high societal relevance, as it contributes to improved mitigation of climate change consequences where the effect of measures can be better observed and anticipated. This regards, for example, the effectiveness of measures against erosion of coasts or of soil in agricultural use.
The project will address the following central objectives:
- Providing an automatic and flexible method of 4D change analysis for different kinds of input data and surface activities by extending recent methods of change analysis for 4D point clouds
- Deriving new information layers from spatiotemporal properties of surface activities for interpretation by analysts, for supporting decision making (e.g., to adapt the observation settings), and for further characterization of underlying surface processes in subsequent analysis
- Enabling automatic classification of surface activities based on spatiotemporal features as characteristic change descriptors using unsupervised and supervised methods
CharAct4D will therefore provide an approach that is transferrable to a broad range of applications of 4D point cloud observation. The project results will enable gaining new insights into patterns of surface dynamics as well as targeted observation of surface processes at the local landscape scale.
Related projects
- 4D Objects-By-Change: Change Analysis of Natural Surfaces using 3D Time Series
- py4dgeo: library for change analysis in 4D point clouds
Related publications
- Zahs, V., Höfle, B., Federer, M., Weiser, H., Tabernig, R. & Anders, K. (2024): Automatic Classification of Surface Activity Types from Geographic 4D Monitoring Combining Virtual Laser Scanning, Change Analysis and Machine Learning. EGU General Assembly 2024. Vol. EGU24, pp. 1-2. DOI: 10.5194/egusphere-egu24-1640.
- Hulskemper, D., Anders, K., Antolínez, J.A.Á., Kuschnerus, M., Höfle, B., Lindenbergh, R. (2022): Characterization of Morphological Surface Activities derived from Near-Continuous Terrestrial LiDAR Time Series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2/W2-2022, 53-60. DOI: 10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022.
