Deep Learning the City: Quantifying Urban Perception at a Global Scale
Name
1608.01769.pdf
Description
Submitted version
Size
8.37 MB
Format
Adobe PDF
Checksum (MD5)
008e7eb9a74dcbb734c410cf52d20722
Author(s)
Dubey, Abhimanyu
Naik, Nikhil
Parikh, Devi
Raskar, Ramesh
Hidalgo, César A.
Date Issued
2016
Publisher
Springer International Publishing
Citation
Dubey, Abhimanyu, Naik, Nikhil, Parikh, Devi, Raskar, Ramesh and Hidalgo, César A. 2016. "Deep Learning the City: Quantifying Urban Perception at a Global Scale."
Version
Original manuscript
Abstract
© Springer International Publishing AG 2016. 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-46448-0_12
