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Artificial Intelligence and Human Senses for the Evaluation of Urban Surroundings

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Intelligent Human Systems Integration 2019 (IHSI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 903))

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Abstract

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.

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Notes

  1. 1.

    https://cloud.google.com/vision/.

  2. 2.

    http://pulse.media.mit.edu/.

References

  1. Kasmar, J.: The development of a usable lexicon of environmental descriptors. Environ. Behav. 2(2), 153–169 (1970)

    Article  Google Scholar 

  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)

    Google Scholar 

  3. Evans, G.W., Smith, C., Pezdek, K.: Cognitive maps and urban form. J. Am. Plan. Assoc. 48(2), 232–244 (1982)

    Article  Google Scholar 

  4. Kaplan, R.: The nature of the view from home: psychological benefits. Environ. Behav. 33(4), 507–542 (2001)

    Article  MathSciNet  Google Scholar 

  5. Nasar, J.L.: Environmental correlates of evaluative appraisals of central business district scenes. Landsc. Urban Plan. 14(C), 117–130 (1987)

    Google Scholar 

  6. Herzog, T.R., Kaplan, S., Kaplan, R.: The prediction of preference for familiar urban places. Environ. Behav. 8(4), 627–645 (1976)

    Article  Google Scholar 

  7. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  8. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  9. Hyam, R.: Automated image sampling and classification can be used to explore perceived naturalness of urban spaces. PLoS One 12(1), e0169357 (2017)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Verma, D., Jana, A., Ramamritham, K.: Quantifying urban surroundings using deep learning techniques: a new proposal. Urban Sci. 2(3), 78 (2018)

    Article  Google Scholar 

  14. Yang, M., Kang, J.: Psychoacoustical evaluation of natural and urban sounds in soundscapes. J. Acoust. Soc. Am. 134(1), 840–851 (2013)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  17. Axelsson, Ö., Nilsson, M.E., Berglund, B.: A principal components model of soundscape perception. J. Acoust. Soc. Am. 128(5), 2836–2846 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Xiao, J., Tait, M., Kang, J.: A perceptual model of smellscape pleasantness. Cities, 0–1 (2018)

    Google Scholar 

  20. McLean, K.: Smellmap: Amsterdam—olfactory art and smell visualization. Leonardo 50(1), 92–93 (2017)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

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).

Author information

Authors and Affiliations

  1. Centre for Urban Science and Engineering, Indian Institute of Technology, Bombay, India

    Deepank Verma & Arnab Jana

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India

    Krithi Ramamritham

Authors
  1. Deepank Verma
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  2. Arnab Jana
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  3. Krithi Ramamritham
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Corresponding author

Correspondence to Deepank Verma .

Editor information

Editors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Waldemar Karwowski

  2. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

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Verma, D., Jana, A., Ramamritham, K. (2019). Artificial Intelligence and Human Senses for the Evaluation of Urban Surroundings. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_130

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