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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.

Image-based unmanned aircraft systems (UASs) are used in a variety of geodetic applications. Precise 3D terrain surface mapping requires ground control points (GCPs) for scaling and (indirect) georeferencing. In image analysis software (e.g., Agisoft Metashape), the images can be generated to a 3D point cloud using Structure-from-Motion (SfM). In general, the conventional GCP design for UAS flights is a checkerboard pattern, which is provided in the software and used for automatic marker detection in each image. When changing the pattern, manual work would be required by picking the GCP individually by hand. To increase the level of automation in the evaluation, this article aims to present a workflow that automatically detects a new edge-based GCP design pattern in the images, calculates their center points, and provides this information to the SfM software. Using the proposed workflow based on deep learning (DL) and image processing, the quality of the resulting 3D model can be equated to the result with GCP center points picked by human evaluator. Consequently, the workload can be accelerated with this approach.