Enhancing Urban Design Through Geodata and Machine Learning

Machine learning (ML) methods have seen surprisingly little application in the innovation-driven field of urban design. Research by Boim, Dortheimer, and Sprecher (2022) presented a novel use of ML to generate alternative urban plans considerate of existing local practices, using data extracted from a GIS package to train a Conditional Generative Adversarial Network (CGAN) model. This paper extends that work with a novel dataset built from open geospatial data from Glasgow with results validated using additional quantitative, and qualitative validation methods. The results show the CGAN model is capable of producing geographic and contextually sympathetic urban design proposals with output quality confirmed by result validation.