Discovering Underground Maps from Fashion
This work addresses the problem of understanding urban characteristics and segmenting cities into meaningful regions for urban planners and sociologists, offering an incremental approach to urban mapping.
This paper proposes a method to automatically create 'underground' neighborhood maps of cities by analyzing the fashion sense of people from publicly available images. The method segments city maps without supervision, showing promising results across 37 cities worldwide when evaluated by human judges and non-image benchmarks.
The fashion sense -- meaning the clothing styles people wear -- in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to automatically create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method finds neighborhoods with a similar fashion sense and segments the map without supervision. For 37 cities worldwide, we show promising results in creating good underground maps, as evaluated using experiments with human judges and underground map benchmarks derived from non-image data. Our approach further allows detecting distinct neighborhoods (what is the most unique region of LA?) and answering analogy questions between cities (what is the "Downtown LA" of Bogota?).