  Computer vision methods that quantify the perception of urban environment are
increasingly being used to study the relationship between a city's physical
appearance and the behavior and health of its residents. Yet, the throughput of
current methods is too limited to quantify the perception of cities across the
world. To tackle this challenge, we introduce a new crowdsourced dataset
containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons
provided by 81,630 online volunteers along six perceptual attributes: safe,
lively, boring, wealthy, depressing, and beautiful. Using this data, we train a
Siamese-like convolutional neural architecture, which learns from a joint
classification and ranking loss, to predict human judgments of pairwise image
comparisons. Our results show that crowdsourcing combined with neural networks
can produce urban perception data at the global scale.
