Accessing eye-level greenness visibility from open-source street view images: A methodological development and implementation in multi-city and multi-country contexts

Ilse Abril Vázquez Sánchez, S. M. Labib*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The urban natural environment provides numerous benefits, including augmenting the aesthetic appeal of urban landscapes and improving mental well-being. While diverse methods have been used to evaluate urban greenery, the assessment of eye-level greenness visibility using street-view level images is emerging due to its greater compatibility with human perception. Many existing studies predominantly rely on proprietary street view images provider such as Google Street View (GSV) data; the usage restrictions and lack of alignment with FAIR (Findability, Accessibility, Interoperability, and Reusability) principles present challenges in using proprietary images at scale. Therefore, incorporating Volunteered Street View Imagery (VSVI) platforms, such as Mapillary, is emerging as a promising alternative. In this study, we present a scalable and reproducible methodological framework for utilising Mapillary images for Green View Index (GVI) assessment using image segmentation approach and evaluate the completeness and usefulness of such data in diverse geographical contexts, including eleven cities (i.e., Amsterdam, Barcelona, Buenos Aires, City of Melbourne, Dhaka, Ho Chi Minh, Kampala, Kobe, Mexico City, Seattle, and Tel Aviv). We also evaluate the use of globally available satellite-based vegetation indices (e.g., Normalised Difference Vegetation Index-NDVI) to estimate GVI in locations where street-view images are unavailable. Our approach demonstrates the applicability of Mapillary data for GVI assessments, although revelling considerable disparities in image availability and usability between cities located in developed and developing countries. We also identified that the NDVI could be used effectively to estimate GVI values in locations where direct street-level imagery is limited. Additionally, the analysis reveals notable differences in greenness visibility across cities, particularly in high-density, lower-income cities in Africa and South Asia, compared to low-density, high-income cities in the USA and Europe.

Original languageEnglish
Article number105262
Pages (from-to)1-17
Number of pages17
JournalSustainable Cities and Society
Volume103
DOIs
Publication statusPublished - Apr 2024

Keywords

  • Google Street View
  • Green Infrastructure
  • Green View Index
  • Greenspace
  • Image segmentation
  • Normalised Difference Vegetation Index

Fingerprint

Dive into the research topics of 'Accessing eye-level greenness visibility from open-source street view images: A methodological development and implementation in multi-city and multi-country contexts'. Together they form a unique fingerprint.

Cite this