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• W4387310872 abstract "Abstract The urban natural environment provides numerous benefits, including augmenting the aesthetic appeal of urban landscapes and improving mental wellbeing. 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 ( F indability, A ccessibility, I nteroperability, and R eusability) 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 seven cities (i.e., Amsterdam, City of Melbourne, Dhaka, Kampala, 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." @default.
• W4387310872 created "2023-10-04" @default.
• W4387310872 creator A5054067770 @default.
• W4387310872 creator A5092995343 @default.
• W4387310872 date "2023-10-03" @default.
• W4387310872 modified "2023-10-05" @default.
• W4387310872 title "Accessing Eye-level Greenness Visibility from Open-Source Street View Images: A methodological development and implementation in multi-city and multi-country contexts" @default.
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• W4387310872 doi "https://doi.org/10.21203/rs.3.rs-3400487/v1" @default.
• W4387310872 hasPublicationYear "2023" @default.
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