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Abstract Geodetic monitoring measurements (e.g., of terrain surfaces) are used to detect deformations. Terrestrial laser scanning (TLS) or unmanned aircraft systems (UAS) equipped with lightweight cameras are often utilized for land surveying, resulting in point clouds that represent the surface of the captured object. For image-based acquisition of the area of interest, point clouds must first be generated from overlapping images, for which the Structure-from-Motion (SfM) method is commonly used. To perform deformation analyses and derive changes from them, at least two temporally different measurement epochs of the same area are required. In this article, we present both point cloud- and feature-based models from TLS and SfM-based UAS point clouds. In addition, an image-based 2D approach using optical flow is applied as an example for landslide simulation to detect changes on object surfaces. To eliminate erroneous results in the analyses due to vegetation areas, the 3D data is filtered using the CANUPO algorithm. The results of this research study show, that the task of deformation detection has some challenges, depending on the use case and the methodology. The point cloud-based methods are suitable to detect pure changes between two point clouds. Also, the direction of these changes can be determined to distinguish between material uplift and downlift. In contrast, the feature-based descriptor (Fast Point Feature Histogram, FPFH) assigns pairs of points between two epochs based on similar geometry in both point clouds therewith individual movements can be detected. However, areas that have changed significantly cannot be assigned. Optical flow shows point changes in similar dimensions to the target deformations and allows deformation analysis with much less computational effort than with 3D point clouds. Considering these findings, point cloud-based method are suitable for determining surface-based information, while the feature-based and image-based methods are capable of extracting local changes.

Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations. While there are different plots available to visualize Shapley values, there is a lack of suitable visualization for geospatial use cases, resulting in the loss of the geospatial context in traditional plots. This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrate its feasibility through an exemplary use case—predicting bike activity in a rental bike system. The visualizations show that visualizing Shapley values on geographic maps can provide valuable insights that are not visible in traditional plots for Shapley additive explanations. Geovisualizations are recommended for explaining machine learning models in geospatial applications or for extracting knowledge about real-world applications. Suitable visualizations for the considered use case are a proportional symbol map and a mapping of computed Voronoi values to the street network.