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 project 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 project 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.
Process 1: Inputs
How can urban theory and history inform the data inputs for computational models of cities?
By training a convolutional neural net to classify the elements of the city image from geocoded and labeled images from the Kevin Lynch archive (accessed via Flickr API), we can combine historical and theoretical perspective with new digital approaches.
Images from the Kevin Lynch archive were geocoded and labeled using a re-projection (created in GIS) of his original city image diagram. The resulting geocoded images can be viewed online.
Process 2: Instrument
How can new digital methods extend the knowledge of urbanism and design practice?
By building a device to capture 360-degree imagery, associated to a GPS tag, we can build our own dataset to represent the city of Boston today.
Process 3: Interface
How can digital methods capture human perspective, to inform both urbanism and computation?
By developing an interface for crowdsourced labeling, we can collect human perception at scale. These label can be used to inform machine learning models, and can voluntarily be attributed to their contributors.