Urban areas are home to over half of the global population and consume significant natural resources, necessitating effective city management for urban resilience and sustainability. Resilient cities are better prepared to face challenges, from natural disasters to economic downturns, and adapt to climate change, resource scarcity, and population growth. Urban digital twins, which are virtual replicas of cities integrating data from various sources, play a pivotal role in enhancing and supporting urban resilience. These digital twins empower urban planners, designers, and the public to make informed decisions, identify vulnerabilities, and respond to acute shocks in real-time. Leveraging technologies like the Internet of Things (IoT) and 3D city models, urban digital twins offer a dynamic representation of the city’s functions, allowing for better visualization, monitoring, and decision-making. However, challenges related to interoperability and data acquisition need to be addressed to maximize the potential of urban digital twins. This thesis investigates how environmental sensor data can be collected, processed, and integrated into 3D city models to visualize the dynamic elements of a city comprehensively. It explores the use of international standards, such as the OGC SensorThings API standard to acquire, store, and manipulate real-time and historical crowd-source, sensor data in a consistent and interoperable manner. The concepts of sensor data pre-processing and interpolating are also explored through OGC standards, in the context of providing detailed spatio-temporal information for the urban environment, while the results are visualized in a 3D web application.

Currently more than 4 billion people live in urban areas around the globe, a trend that is expected to be increased in the upcoming years. While urbanisation provides the space for innovation and new opportunities, in the meantime physical, technical and social challenges are rising and the cities’ vulnerability is increasing. A tool to tackle these issues are Computational Fluid Dynamics (CFD) simulations, which can provide insight in various topics. CFD simulations are valuable for modelling complex urban phenomena such as wind flow, microclimates and thermal comfort. A CFD requires as an input a 3D geometric dataset that represents objects in the urban environment which are most commonly buildings and then according to this input the air flow is simulated around it. When creating geometries automatically for CFD simulations, several clean up tasks must be completed for them to be usable without any issues. One of the problems arising is related to the redundant faces shared between adjacent buildings, which have no purpose for outdoor flow simulations and cause complications when creating the mesh that is needed for the CFD. This synthesis project focuses on addressing the aforementioned issue by removing the shared faces. The ultimate goal of this project was to create an open-source product that can efficiently and in an automated way remove the adjacent faces between buildings. The benefits will be imminent during the meshing process, as we strive to reduce the time that consultancies spend fixing the input geometries before running a CFD simulation, along with an overall improved user experience. This report is organised in four main sections. The first section is the general introduction of the issue that needs to resolved. The second section defines more in depth the problem and sets the research questions, in accordance to that, in the third section the research methodology is developed. In the fourth section the results of both methods are presented. The fifth sectionfocuses on a reflection of the project, while the sixth section presents the final conclusions. Finally, the seventh section contains the specifics of the project management itself. The project was carried out in cooperation with Dassault Syst`emes and is developed in the context of the GEO1101 course in MSc Geomatics TU Delft. In addition to this report we have created a GitHub repository (https://github.com/Fabisser/facesBgone) that contains the source code of the two methods.