The urban river space is the area in the city surrounding a river, distinguishing itself from other parts of the city by this relationship with the water. Urban river spaces around the world are increasingly under development and being regenerated. Urban planning solutions necessitate a comprehensive overview of this space, their boundaries and their characteristics. However, the concept of the urban river space is ambiguously defined, with varying definitions across studies. This thesis addresses the need for a standardized approach through semantic urban river space delineation to facilitate cross-case analysis. Three delineation methods are proposed and applied to urban areas in the Netherlands: the first building line based on visible building nodes, the visible space derived from viewshed analysis, and the floodable area, based on 100-year flood depth data. As urban river spaces are often represented as cross-sectional segments in research, this segment is used as unit to develop a typology of urban river spaces in the Netherlands. Properties of the segment, such as elevation, landuse, vegetation, flood risk, and visibility are quantified and used as input for the k-means clustering algorithm. 10 clusters are derived, each representing a semantic type of river space, resulting in a data-driven typology that enhances the understanding of urban river spaces in the Netherlands.

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.