Investigating pedestrian-level greenery in urban forms in a high-density city for urban planning

Abstract

The understanding of pedestrian-level greenery across urban forms in built environment configurations in high-density cities is insufficient. We conducted a citywide investigation of urban greenery from the pedestrian perspective by developing a deep learning technique to extract greenery from fisheye images generated from Google Street View images in Hong Kong. Relying on open-source data, we compared pedestrian-level greenery measurements with the satellite-based normalized difference vegetation index (NDVI) in diverse urban forms represented by local climate zone classes. Street greenery was spatially variant, and low greenery was found predominantly in private residential and commercial/business lands in high-density areas. Pedestrian-level measurement and the NDVI were strongly correlated, but the inconsistency between them increased from high- and mid-rise forms to low-rise forms and from compact forms to open forms. We also demonstrated the idea of integrating nearby street greenery with spatial information on population and urban morphology for inequality analysis. Potential implications for urban planning are provided. The findings linking street greenery with urban morphology are useful for urban and greenery planning in climate-resilient, sustainable, and healthy cities. Our analytical approach using open-source data is transferable to other high-density cities.