Next Article in Journal
Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the US National Household Travel Survey
Previous Article in Journal
Principles for Integrating the Implementation of the Sustainable Development Goals in Cities
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Urban Sci. 2018, 2(3), 78; https://doi.org/10.3390/urbansci2030078

Quantifying Urban Surroundings Using Deep Learning Techniques: A New Proposal

1
Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
2
Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
*
Author to whom correspondence should be addressed.
Received: 28 July 2018 / Revised: 23 August 2018 / Accepted: 26 August 2018 / Published: 28 August 2018
Full-Text   |   PDF [4779 KB, uploaded 28 August 2018]   |  

Abstract

The assessments on human perception of urban spaces are essential for the management and upkeep of surroundings. A large part of the previous studies is dedicated towards the visual appreciation and judgement of various physical features present in the surroundings. Visual qualities of the environment stimulate feelings of safety, pleasure, and belongingness. Scaling such assessments to cover city boundaries necessitates the assistance of state-of-the-art computer vision techniques. We developed a mobile-based application to collect visual datasets in the form of street-level imagery with the help of volunteers. We further utilised the potential of deep learning-based image analysis techniques in gaining insights into such datasets. In addition, we explained our findings with the help of environment variables which are related to individual satisfaction and wellbeing. View Full-Text
Keywords: environment perception; deep learning; WebRTC; object detection; semantic segmentation; image classification; QGIS environment perception; deep learning; WebRTC; object detection; semantic segmentation; image classification; QGIS
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Verma, D.; Jana, A.; Ramamritham, K. Quantifying Urban Surroundings Using Deep Learning Techniques: A New Proposal. Urban Sci. 2018, 2, 78.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Urban Sci. EISSN 2413-8851 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top