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Analyzing the age-friendliness of the urban environment using computer vision methods

Author

Listed:
  • Fereshteh Moradi
  • Nimish Biloria
  • Mukesh Prasad

Abstract

The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.

Suggested Citation

  • Fereshteh Moradi & Nimish Biloria & Mukesh Prasad, 2023. "Analyzing the age-friendliness of the urban environment using computer vision methods," Environment and Planning B, , vol. 50(8), pages 2294-2308, October.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:8:p:2294-2308
    DOI: 10.1177/23998083231153862
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    References listed on IDEAS

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    1. Mark Gorman & Sion Jones & Jeffrey Turner, 2019. "Older People, Mobility and Transport in Low- and Middle-Income Countries: A Review of the Research," Sustainability, MDPI, vol. 11(21), pages 1-15, November.
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