Artificial Intelligence and Human Senses for the Evaluation of Urban Surroundings

  • Deepank VermaEmail author
  • Arnab Jana
  • Krithi Ramamritham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


Traditional city planning and design tools require major restructuring. Even with the rapid growth in the availability of mobile communication devices, connectivity, data generation, and analysis tools, the idea of the creation of citizen-centric and smart cities has not been fully conceptualized. Individual perception and preferences toward urban spaces play an important role in mental satisfaction and wellbeing. However, the notion has not been studied and experimented along with various planning instruments. This study discusses the recent studies involving Artificial intelligence tools and sensory data collection. This paper further comment on the integrated methodology to collect sensory datasets that will further help in the evaluation of urban surroundings with individual perspectives.


Urban perception Deep learning Sensory datasets City planning 



The authors would like to thank the Ministry of Human Resource Development (MHRD), India and Industrial Research and Consultancy Centre (IRCC), IIT Bombay for funding this study under the grant titled Frontier Areas of Science and Technology (FAST), Centre of Excellence in Urban Science and Engineering (grant number 14MHRD005).


  1. 1.
    Kasmar, J.: The development of a usable lexicon of environmental descriptors. Environ. Behav. 2(2), 153–169 (1970)CrossRefGoogle Scholar
  2. 2.
    Nasar, J.L.: Perception, cognition, and evaluation of urban places. In: Altman, I., Zube, E.H. (eds.) Public Places and Spaces, pp. 31–56. Springer, US, Boston, MA (1989)CrossRefGoogle Scholar
  3. 3.
    Evans, G.W., Smith, C., Pezdek, K.: Cognitive maps and urban form. J. Am. Plan. Assoc. 48(2), 232–244 (1982)CrossRefGoogle Scholar
  4. 4.
    Kaplan, R.: The nature of the view from home: psychological benefits. Environ. Behav. 33(4), 507–542 (2001)CrossRefGoogle Scholar
  5. 5.
    Nasar, J.L.: Environmental correlates of evaluative appraisals of central business district scenes. Landsc. Urban Plan. 14(C), 117–130 (1987)CrossRefGoogle Scholar
  6. 6.
    Herzog, T.R., Kaplan, S., Kaplan, R.: The prediction of preference for familiar urban places. Environ. Behav. 8(4), 627–645 (1976)CrossRefGoogle Scholar
  7. 7.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)Google Scholar
  8. 8.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  9. 9.
    Hyam, R.: Automated image sampling and classification can be used to explore perceived naturalness of urban spaces. PLoS One 12(1), e0169357 (2017)CrossRefGoogle Scholar
  10. 10.
    Shen, Q., et al.: StreetVizor: visual exploration of human-scale urban forms based on street views. IEEE Trans. Vis. Comput. Graph. 24(1), 1004–1013 (2018)CrossRefGoogle Scholar
  11. 11.
    Salesses, P., Schechtner, K., Hidalgo, C.A.: The collaborative image of the city: mapping the inequality of urban perception. PLoS One 8(7), e68400 (2013)CrossRefGoogle Scholar
  12. 12.
    Liu, L., Wang, H., Wu, C.: A machine learning method for the large-scale evaluation of urban visual environment. Comput. Res. Repos (ArXiv) (2016)Google Scholar
  13. 13.
    Verma, D., Jana, A., Ramamritham, K.: Quantifying urban surroundings using deep learning techniques: a new proposal. Urban Sci. 2(3), 78 (2018)CrossRefGoogle Scholar
  14. 14.
    Yang, M., Kang, J.: Psychoacoustical evaluation of natural and urban sounds in soundscapes. J. Acoust. Soc. Am. 134(1), 840–851 (2013)CrossRefGoogle Scholar
  15. 15.
    Cakir, E., Parascandolo, G., Heittola, T., Huttunen, H., Virtanen, T.: Convolutional recurrent neural networks for polyphonic sound event detection. IEEE/ACM Trans. Audio Speech Lang. Process. 25(6), 1291–1303 (2017)CrossRefGoogle Scholar
  16. 16.
    Hong, J.Y., Jeon, J.Y.: Exploring spatial relationships among soundscape variables in urban areas: a spatial statistical modelling approach. Landsc. Urban Plan. 157, 352–364 (2017)CrossRefGoogle Scholar
  17. 17.
    Axelsson, Ö., Nilsson, M.E., Berglund, B.: A principal components model of soundscape perception. J. Acoust. Soc. Am. 128(5), 2836–2846 (2010)CrossRefGoogle Scholar
  18. 18.
    Yu, L., Kang, J.: Modeling subjective evaluation of soundscape quality in urban open spaces: an artificial neural network approach. J. Acoust. Soc. Am. 126(3), 1163–1174 (2009)CrossRefGoogle Scholar
  19. 19.
    Xiao, J., Tait, M., Kang, J.: A perceptual model of smellscape pleasantness. Cities, 0–1 (2018)Google Scholar
  20. 20.
    McLean, K.: Smellmap: Amsterdam—olfactory art and smell visualization. Leonardo 50(1), 92–93 (2017)CrossRefGoogle Scholar
  21. 21.
    Quercia, D., Schifanella, R., Aiello, L.M., McLean, K.: Smelly maps: the digital life of urban smellscapes. Jacobs 1961 (May 2015)Google Scholar
  22. 22.
    Aiello, L.M., Schifanella, R., Quercia, D., Aletta, F.: Chatty maps: constructing sound maps of urban areas from social media data. R. Soc. Open Sci. 3(3), 150690 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Urban Science and Engineering, Indian Institute of TechnologyBombayIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyBombayIndia

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