Establishing a Reliable Assessment of the Green View Index Based on Image Classification Techniques, Estimation, and a Hypothesis Testing Route

Yiming Liu, Xiangxiang Pan, Qing Liu and Guicai Li ()
Additional contact information
Yiming Liu: Guangdong Academy of Social Sciences, Guangzhou 510635, China
Xiangxiang Pan: Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Qing Liu: Laboratory for Urban Future, Peking University, Shenzhen 518055, China
Guicai Li: Shenzhen Graduate School, Peking University, Shenzhen 518055, China

Land, 2023, vol. 12, issue 5, 1-14

Abstract: Sustainable development policies and spatial planning for maintaining greenery are crucial for all major cities in the world, and the measurement of green space indicators in planning practice needs to evolve in response to the demands of the times and technological drivers. This study explores an informal urban green space indicator, the green view index ( GVI ), which uses the visual perception of an observer to measure the quality of urban space by simulating the pedestrian perspective of the road in street-view image data and then calculating the proportion of vegetation in the road landscape. The GVI is different from macro indicators, such as public recreational green space, forest coverage, and green space rate, which are derived from planning data or remote sensing data in traditional urban planning; it starts from the bottom-up perception of individual residents and is more relevant to their subjective demands. At present, most international cities have made outstanding achievements in controlling public recreational green space, forest coverage, green space rates, and other macrolevel indicators of urban spatial quality; however, with the promotion of the concept of “human-oriented” urban planning, the potential restoration of urban spatial quality at the microlevel is gradually being recognized. To ensure the efficiency and reliability of this study, inspired by computer vision techniques and related GVI studies, a research method based on chromaticity was built to identify the proportions of green vegetation in street view images, and the credibility was improved by eliminating unreliable data. By using this method, we could evaluate a city at an overall scale instead of the previous block scale. The final research result showed that Shenzhen is friendly to human visual senses, and the GVI of the streets in developed areas is generally higher than that in developing areas. The geostatistical analysis of the green viewpoint data provides a more intuitive guide for researchers and planners, and it is believed to inform the planning and design of environmentally friendly, smart, and sustainable future cities.

Keywords: the green view index; street-view map; chromaticity; credibility (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/5/1030/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/5/1030/ (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:12:y:2023:i:5:p:1030-:d:1141648

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 ().