TY - JOUR
T1 - Accessing eye-level greenness visibility from open-source street view images
T2 - A methodological development and implementation in multi-city and multi-country contexts
AU - Sánchez, Ilse Abril Vázquez
AU - Labib, S. M.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - Google Street View
KW - Green Infrastructure
KW - Green View Index
KW - Greenspace
KW - Image segmentation
KW - Normalised Difference Vegetation Index
UR - http://www.scopus.com/inward/record.url?scp=85185268884&partnerID=8YFLogxK
U2 - 10.1016/j.scs.2024.105262
DO - 10.1016/j.scs.2024.105262
M3 - Article
AN - SCOPUS:85185268884
SN - 2210-6707
VL - 103
SP - 1
EP - 17
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 105262
ER -