Deep Learning the City: Quantifying Urban Perception at a Global Scale

  • Abhimanyu Dubey
  • Nikhil Naik
  • Devi Parikh
  • Ramesh Raskar
  • César A. Hidalgo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9905)

Abstract

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.

Keywords

Perception Attributes Street view Crowdsourcing 

Supplementary material

419956_1_En_12_MOESM1_ESM.pdf (19.2 mb)
Supplementary material 1 (pdf 19651 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Abhimanyu Dubey
    • 1
  • Nikhil Naik
    • 3
  • Devi Parikh
    • 2
  • Ramesh Raskar
    • 3
  • César A. Hidalgo
    • 3
  1. 1.Indian Institute of TechnologyDelhiIndia
  2. 2.Virginia TechBlacksburgUSA
  3. 3.MIT Media LabCambridgeUSA

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