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

Nicolás Valenzuela-Levi (), Nicolás Gálvez Ramírez, Cristóbal Nilo, Javiera Ponce-Méndez, Werner Kristjanpoller, Marcos Zúñiga and Nicolás Torres
Additional contact information
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

Land, 2024, vol. 13, issue 8, 1-19

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.

Keywords: artificial intelligence; big data; smart cities; internet of things; industry 4.0; sustainability (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/8/1211/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/8/1211/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:8:p:1211-:d:1450685

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().