by Brian Ho (MDE '18)
Making a New City Image explores the machine-mediated perception of urban form: new ways of seeing, understanding and experiencing cities in the information age.
Spanning the disciplines of urbanism and computer science, this thesis fosters a productive dialogue between the two. What might we learn about the city using computer vision, deep learning and data science? How might the history, theory and practice of urbanism — which has long viewed the city as a subject of measurement — inform modern applications of computation to cities? Most importantly, can we ensure that both disciplines understand the city as it is perceived by people?
This thesis also formalizes a practice of computer vision cartography. First, it has revisited Kevin Lynch’s Image of the City: applying machine learning models to archival photographs and historical maps of Boston, and classifying from them Lynch’s five elements of the city image. This process has also been adapted to the present day. I have created instruments and devices for the procedural capture of street-level imagery (on bike or by foot), and the automated identification of new categories of urban environments crowdsourced from human input.
Together, these methods produce a new mode of analysis that balances a comprehensive perspective at the scale of the city with a focus on the texture, color and details of urban life.