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A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning

Author

Listed:
  • Nicolás Valenzuela-Levi

    (Department of Architecture, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

  • Nicolás Gálvez Ramírez

    (Department of Electronics, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

  • Cristóbal Nilo

    (Department of Electronics, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

  • Javiera Ponce-Méndez

    (Department of Architecture, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

  • Werner Kristjanpoller

    (Department of Industries, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso 2580816, Chile)

  • Marcos Zúñiga

    (Department of Electronics, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

  • Nicolás Torres

    (Department of Electronics, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago 8940897, Chile)

Abstract

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway.

Suggested Citation

  • Nicolás Valenzuela-Levi & Nicolás Gálvez Ramírez & Cristóbal Nilo & Javiera Ponce-Méndez & Werner Kristjanpoller & Marcos Zúñiga & Nicolás Torres, 2024. "A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning," Land, MDPI, vol. 13(8), pages 1-19, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:8:p:1211-:d:1450685
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    References listed on IDEAS

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    1. Duignan, M.B. & Down, S. & O'Brien, D., 2020. "Entrepreneurial leveraging in liminoidal olympic transit zones," Annals of Tourism Research, Elsevier, vol. 80(C).
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