While reconstructing urban environments, the accuracy of the model is strongly related to the quality of the input data. The initial type of input data for this process could be the footprints of the buildings, which are typically used to reconstruct the buildings, either through manual or automated methods. However, these data could be biased, and when we perform a Computational fluid dynamics (CFD) simulation, they could impact our results. An example of such bias could be either translation or rotation. In our case, simple building footprints were used to define these biases associated with the uncertainty of raw data. Additionally, statistical analysis for representing and quantifying this uncertainty was performed.
The Case C dataset from the Architectural Institute of Japan (AIJ) is an example of a canonical case for our simulations, in which the building footprints could play an important role in the calculation of our results. A CFD simulation on such a canonical case would typically involve the steps of preparing the geometry, generating the mesh, setting boundary and initial conditions, validating the results using experimental data and finally, performing uncertainty analysis. The last step involves various ways of representing the results, like scatter plots, box plots and contour plots. Important aspects of this analysis were the visualization of the flow patterns, the calculation of various quantities of interest such as velocity or turbulent kinetic energy, and the comparison of the simulation results with the experimental data from the AIJ dataset. The effects were examined across multiple wind directions and different footprint uncertainties. This approach could help us to improve the accuracy and reliability of the CFD simulations.

The NMCAs (National Mapping and Cadastral Agencies) of European countries have different cadastral survey accuracy standards (European Global Navigation Satellite Systems Agency, 2019). In order to meet these standards, the appropriate equipment and services should be determined. The augmentation service Galileo High Accuracy Service (HAS), that is planned for 2022, will provide high accuracy Precise Point Positioning (PPP) corrections. Unlike other high-accuracy services, the Galileo HAS will be free of charge and available worldwide, without the need to be close to a base station or to a dedicated provider network. The PPP corrections will be provided through the Galileo signal as well as through the Internet (EUSPA, 2021). Because of the potential of the Galileo HAS, for the Synthesis Project we want to get insight in the accuracy of the augmentation service. Since a big share of cadastral surveys is performed in the built environment, we also want to determine the accuracy in an urban canyon. With the found accuracy, we can possibly judge whether Galileo HAS is suitable for cadastral surveys in the Netherlands, by comparing the measured accuracy to cadastral survey accuracy standards of the Dutch Kadaster.
As a final conclusion for this project, Galileo HAS is still a technique under development and the PPP-based correction methods are currently not as accurate as the RTK-based ones. Galileo HAS will present in the future ways to correct these errors.