The Sign of the City - Decoding Urban Stories Through AI
The Sign of the City explores how urban signage reveals hidden layers of a city’s cultural, economic, and lexical fabric. Challenging the concepts in Kevin Lynch’s The Image of the City and conventional machine learning methodologies in urban research, the project leverages AI and deep learning to analyze the types, languages, and text on signs in street view images, uncovering financial, cultural, and lexical layers of urban environments. It examines over 8,000 Google Street View images from 46 neighborhoods across New York City.
Through building a city-specific training dataset by labeling thousands of street view images across various urban layers, the project trained a YOLOv8 model to detect 20 types of signage. After detection, Optical Character Recognition (OCR) extracted text and identified eight languages, uncovering cultural, economic, and regulatory insights embedded in the city’s signage.
For example, shop signs indicate high commercial activity, “For Lease” signs reflect potential vacancies, and “Do Not…” signs suggest stricter regulations. The languages on signs illustrate ethnic patterns and emerging cultural hubs, while word choices like “best,” “lucky,” and “international” offer glimpses into the lexicons of different communities.
The findings are presented through an interactive web interface, simulating a virtual walk through New York’s streets. Users can explore signage data layered with demographic insights, such as race and income, creating a nuanced understanding of each community.
This project extends the tradition of urban visual studies by incorporating AI to uncover the intangible aspects of city life—its stories, transitions, and identities. It offers new tools for urban analysis, empowering planners, researchers, and communities to reimagine the city’s fabric through its signs.
