There has been an ever increasing focus on the development of smart cities, both by big corporate in the field of Information Communication and Technology as well as respective governments responsible for these cities worldwide. This in combination with the influx of easily accessible data owing to cheaper and efficient sensors has risen to the concept of digital twinning of urban spaces and cities.

Urban digital twins require the city to be represented in 3-dimensional digitally, these datasets are oftentimes too large to be provided on as-is basis for most platforms. To tackle this problem of large 3D datasets, the OGC 3D tiling specifications were formed and accepted in 2019.

This thesis attempts to develop a neighbourhood designing platform which would enable users to design neighbourhoods in 3D while being able to experience the design in an immersive manner by utilizing virtual reality’s capability to observe 3D assets in scales which are not possible using a physical model or a 2 dimensional neighbourhood plan, employing the OGC 3D tiling technique to understand the extent to which a combination of existing 3D datasets can be made with a user-interactive neighbourhood designing platform in a VR environment.

The results of the study reveal that using our methodology, it is possible to combine existing 3D datasets with user made neighbourhood design with their own 3D assets of any level of detail and furthermore, it is possible to utilize instanced tiling format to disseminate the neighbourhood design as a standard 3D tiled design.

Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This project’s main objective is to analyse how ArcGIS Indoors can be used with location awareness methods to elaborate and develop space management tools for COVID­-19 restrictions in order to reopen the workspace for TU Delft Campus. This was accomplished by using six Arduino micro controllers, which were programmed in C++ to scan all available Wi-­Fi fingerprints in the east wing of the Faculty of Architecture and the Built Environment of TU Delft and send over the data to an ArcGIS Indoor Information Model (AIIM). The data stored on the AIIM is then accessed using the app on the user’s Android device using REST Application Programming Interface (API) where a kNN based matching algorithm then identifies the location of the user. The results show that the localisation is not consistent for rooms that are directly above each other or share common access points. However, when functioning to locate different tables inside a room, the system proved to uniquely distinguish between the specific tables. As a result, we can conclude that based on the size of the rooms, more Arduino devices should be installed to achieve an ideal accuracy. Finally, recommendations are made for the continuation of this research.