3D city models are continuously becoming more popular among practitioners due to the volume and versatility of information they contain, which makes them suitable to be used in various applications. However, there is no mechanism to allow maintaining them updated at the same pace that cities evolve, or when error correction is necessary, eventually diminishing their value. Many cities around the world already possess such models which are mostly used for experimentation and research purposes. Such an example is also the city of Rotterdam, whose 3D city model is not regularly updated and has to be outsourced for that purpose. This thesis investigates into addressing this issue by proposing and implementing an integrative maintenance workflow. The workflow is designed to fulfill what the maintenance needs of a typical municipality are expected to be. Those needs were identified after conducting an analysis of the current situation and collecting information from practitioners within the municipality through interviews. The workflow is a combination of 3D city model versioning and visual editing capabilities with the aim to effectively maintain CityJSON encoded models in an intuitive way. Its implementation includes two prototype software implementations: a versioning component, which is utilized to create a workflow inspired by git flow and allows concurrent maintenance and alternative scenario testing in a non-linear and distributed way, and a visual editing component capable of editing CityJSON encoded 3D city models by extending Blender’s functionality. Following the implementation, the workflow was tested by simulating real world maintenance scenarios. The tests demonstrate the feasibility of maintaining 3D city models with such a workflow and more specifically the suitability of git based workflows. At the same time some key parameters of the versioning mechanism are identified which if tuned properly they can optimize the performance, behavior and robustness of 3D city model versioning. With both components being prototype solutions the workflow is far from operational and there is certainly a lot of space for improvement regarding both components. Utilizing the workflow in practice would be the ideal way for collecting useful feedback. Besides that, there are already extensions of Blender that combined with the visual updating component of the workflow can offer advanced integration of editing and analysis capabilities.

In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model. The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM). Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge. The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well. In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project. We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.