Laura Raddatz’s research while affiliated with University of Applied Sciences Mainz and other places

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.

In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts.

Motivated by the increased use of 3D acquisition of objects by cultural heritage institutions, we were investigating ontologies and metadata schemes for the acquisition process to provide details about the 3D capturing, which can be combined with preexisting ontologies describing an object. Therefore we divided the 3D capturing workflow into common steps starting with the object being placed in front of a 3D scanner to preparation and publication of the 3D datasets and/or derived images. While the proposed ontology is well defined on a coarse level of detail for very different techniques, e.g. Stucture from Motion and LiDAR we elaborated the metadata scheme in very fine detail for 3D scanners available at our institutions. This includes practical experiments with measurement data from past and current projects including datasets published at Zenodo as guiding examples and the source code for their computation. Additionally, the free and Open Source GigaMesh Software Framework's analysis and processing methods have been extended to provide metadata about the 3D processing steps like mesh cleaning as well as 2D image generation. Finally, we discuss the current limitations and give an outlook about future extensions.

Imaging techniques were used to document and monitor physical damage to the unique wall paintings at the Château de Germolles, Burgundy, France. Photogrammetry combined with scanning are the most appropriate techniques to monitor the evolution of microcrack networks in the cornice overhanging the paintings and preserved as a witness to 19th century additions.