While three-dimensional coral reef models are valuable for various applications, existing approaches like photogrammetric scanning and manual modeling require substantial time and expertise, limiting their scalability. Previous algorithmic approaches, particularly Agent-Based Models (ABM), have relied heavily on complex ecological simulations and deep domain knowledge. This thesis explores an alternative data-driven approach to automated coral reef modeling, investigating whether empirical data sources can provide a scalable method for generating ecologically plausible 3D models. Rather than simulating ecological processes from first principles, we develop a pipeline that leverages observational data to inform and constrain procedural generation techniques. Through systematic evaluation of available data sources, including the Global Biodiversity Information Facility (GBIF), CoralNet, the Allen Coral Atlas, the Coral Traits Database and the Smithsonian Institution's 3D coral collection, we identified both opportunities and significant limitations in current data availability. The research developed a modular pipeline implemented in Blender that combines procedural terrain generation with the placement of 3D coral models, integrating species occurrence data aggregated over geomorphic zones. To ensure robust data integration across sources and maintain compatibility with evolving taxonomic standards, the pipeline implements automated species name verification through the World Register of Marine Species (WoRMS) (WoRMS - World Register Of Marine Species, n.d.) API. While the resulting pipeline successfully establishes a foundation for automated coral reef modeling, limitations in available structural data necessitated the use of manually configured parameters for critical aspects such as terrain characteristics and population density. The pipeline's modular structure, standardized taxonomy handling, and integration with standardized classification systems position it well for future iterations as improved data sources become available. This research demonstrates the potential of data-driven approaches to coral reef modeling while highlighting the need for more comprehensive, fine-scale structural data to enable fully automated, ecologically plausible modeling of coral reef environments.

The declining availability of practical hours for medical anatomical education has prompted Enatom to develop a digital anatomical platform, utilizing the open-source WebGL-based point cloud renderer Potree. This platform, which employs detailed point cloud scans of anatomical structures, aims to offer a dynamic and interactive educational experience. Although Enatom's focus is not directly on geomatics, the techniques employed in this project have strong parallels with those used in geomatics, thereby enabling a symbiotic exchange of expertise. This interdisciplinary approach enhances the development of Enatom’s digital platform, with the potential to contribute to the field of geomatics. To address existing user experience challenges, this project has added a lasso-selection tool tailored for Potree, advanced annotation capabilities, and methods for sensitive data anonymization within the point cloud. The project's outcomes will be available in an open-source format at https://github.com/GEO1101-Synthesis-Group4/Selection-Annotation-Repo. This project exemplifies the versatile application of geomatics expertise beyond its traditional scope, demonstrating its potential in enhancing diverse domains such as medical education.

While deep neural networks show great potential for being part of safety-critical applications such as autonomous driving, covering their sensitivity to illumination shifts by adding training data is of- ten non-trivial. The undesired illumination shift between train and test data can be addressed by domain adaptation methods. Recent work [9] has demonstrated performance improvements with a novel zero-shot domain adaptation setting by in- troducing a physics-based visual inductive prior - a trainable Color Invariant Convolution (CIConv) layer - aiming to transform its input to a more do- main invariant representation.
We compare the performance of image classifica- tion for day-night domain adaptation in the zero- shot and the unsupervised setting, and explore the effectiveness of using CIConv in both settings. We show that unsupervised domain adaptation reduces the day-night distribution shift similarly to CIConv in the zero-shot setting. We demonstrate improved performance when CIConv and unsupervised day- night domain adaptation are combined.

The principle of a mirror anamorphosis relies on distortion caused by a reflective object of a particular shape and the perspective of a viewer looking into the reflective object to look at an image on the surface. The distortion of the reflected image is intended to form a recognizable image, while the image on the surface looks completely different. There are many ways a computer could aid the creation of this form of art by determining how the image is being distorted, but in most cases a regular artist would not know specific geometric details of the scene. In these cases a solution is for the creator to provide any image on the surface and an image of its distortion caused by the scene. The creator can match points between both images to guide the computer into calculating the (estimated) distortion.