Decoding the City of Paper : A Navigation of Urban Policy with AI Workflows

This thesis is an exploration of Artificial Intelligence’s (AI) application to the built environment and its implication to architecture. The first half of the thesis explores the utilization of AI-assisted tools to decode the bureaucratic language of zoning regulations, as determined by municipalities in Metro Vancouver, in a format that can be easily understood by both the public and the designers involved. The prototype is designed to be deployed during the pre-application process to foster meaningful conversations between city planners, designers, and the public during the consultative stage. With the advent of powerful AI tools that can decode city plans in visually revealing and simplified language, it creates a mutually beneficial situation where the public’s concerns can be addressed early on, and designers can be equipped with a deeper understanding of the zoning requirements. The second half of the thesis delves into the specificity of AI’s integration into existing architecture design workflow, where a tool can enhance architects’ contextual information to projects. At the present, there are several AI-driven tools that enable architects to perform early massing studies and simulations in the pre-design and schematic design phases. However, for a tool to be contextual relative to the project’s location, additional technicalities are needed to refine its capability as an assistant to the user. As technology in AI progresses, the project also speculates the shift of architect’s role as the ones defining building parameters that serve people, communities, and cities.