Discovering Underground Maps from Fashion

TL;DR

This paper presents an unsupervised method to generate underground city maps by analyzing fashion styles in publicly available images, revealing neighborhood distinctions and cross-city analogies.

Contribution

It introduces a novel approach to map cities underground based on fashion sense, without supervision, and demonstrates its effectiveness across multiple cities.

Findings

Successfully created underground maps for 37 cities

Able to identify distinct neighborhoods and their unique fashion signatures

Enabled cross-city neighborhood analogy reasoning

Abstract

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?).