Urban physics is a multi-scale and interdisciplinary field, that combines science and engineering for the study of physical processes in urban areas. Computational Fluid Dynamics (CFD) is considered a powerful tool for the study of these processes. In the context of microscale phenomena these processes refer to the transfer of heat and mass in the indoor and outdoor urban environment, and can be observed up to ≈1km above the surface of the Earth. Their development takes place inside the Atmospheric Boundary Layer (ABL), which for the case of urban areas is referred to as the Urban Boundary Layer (UBL), and is formed due to the interactions between the surface obstacles and the wind flows. In CFD simulations the surface of the Earth and the encountered obstacles are represented with the use of 3D models, as well as estimated values that are used to implicitly represent their roughness. In this thesis the aim is to investigate relevant to CFD parameters that could be used as 3D model semantics. For this purpose, a list of parameters was identified. From this list roughness length was selected and a methodology was developed for the assignment of roughness values for the open-source software OpenFOAM. The developed methodology that was built using built-in functions of OpenFOAM, is based on an octree data structure that is used to store the input triangulated model in obj format. The roughness length landuse names are stored in an mtl file that complements the input obj and the roughness length values are specified as a user defined parameter using the landuse names. The process is semi-automated and it entails the assignment of non-uniform roughness at the bottom of the computational domain. Additionally, a methodology to assign non-uniform roughness at the inlet of the domain was developed. The results showed that the assignment of non-uniform roughness at the bottom of the domain was successful for cases of flat terrain, however, under the restriction that the input geometry model abided by certain geometry guidelines, such as no self-intersections, no gaps. For the case of non-uniform roughness at the inlet the process also produced satisfactory results in terms of the assignment process, however the impact of multiple roughness values at the inlet on the calculated flow parameters requires further investigation.

Indoor localisation is a highly relevant topic. It can be used for many applications,
such as indoor navigation. Current indoor localisation approaches all have certain
downsides. In this report, the results of a completely new indoor localisation approach are described. The aim of this approach is to perform indoor localisation on room level based on a fingerprint solution using point clouds of the ceilings. The ceiling is used, because the ceiling does generally not change much and therefore it is easier to keep an up-to-date database. This research considers both the use of Dense Image Matching (DIM) input from pictures or videos made with a mobile phone and Light Detection And Ranging (LiDAR) input.