Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index

Xu Chen,Qingyan Meng,Die Hu,Linlin Zhang,Jian Yang

doi:10.3390/f10121109

Abstract

Urban street-side greenery, as an indispensable element of urban green spaces, is beneficial to residents’ physical and mental health. As readily available internet data, street view images have been widely used in urban green spaces research. While the relevant research using multiple images from different directions at a sampling point, researchers need to calculate the index of visible vegetation cover for many times. However, one Baidu panoramic street view image can cover the 360° view similar to that of a pedestrian. In this study, we selected 9644 points at 50-m intervals along the street lines in the central district of Sanya city, China, and acquired panoramic images via the Baidu application programming interface (API). The sky pixels were detected within the Baidu panoramic street view images using a proposed reflectance indicator. The green vegetation was extracted according to the Back Propagation (BP) neural-network method. Our proposed method was validated by comparing the results of the manual recognition and PSPNet method, and the accuracy met the requirements of the study. The Panoramic Green View Index (PGVI) was proposed to quantitatively evaluate greenery around streets. The authors found that the highest frequency value in the distribution was 0.075, which accounted for 32% of the total sample points, and the average PGVI value in this study area was low; the PGVI values between different roads varied greatly, and primary roads tended to have higher PGVI values than other roads. This case study proved that the PGVI is well suited for evaluating greenery around streets. We suggest that the PGVI derived from Baidu panoramic street view images may be a useful tool for city managers to support urban green spaces planning and management.

Highlights

Urbanization, in terms of both population and spatial extent, transforms a landscape from natural cover types to impervious urban land and causes large environmental problems [1,2].Urban green spaces (UGSs) have been recognized as critical landscape design factors in urban environments [3]
According to the street network data downloaded from the Open Street Map (OSM) website, we know that the to the street downloaded website, we know that the typesAccording of roads comprised sixnetwork differentdata categories in the from studythe area, which are motorway, primary types of roads comprised six different categories in and the unclassified study area, which are motorway, primary road, secondary road, tertiary road, residential road, road
The results show that the Panoramic Green View Index (PGVI) values calculated by the authors’ method are consistent with both the manual extraction and the PSPNet methods, which means that the results calculated by the proposed automatic classification method in this paper are of high quality

Summary

Introduction

Urban green spaces (UGSs) have been recognized as critical landscape design factors in urban environments [3]. Aoki et al [23] analyzed the relation between their impressions and the ratio of green within the frame of vision They found that the proportion of green within the field of vision determined by a 28-mm focal distance provided an efficient measure of the respondent’s impression of the amount of greenery as viewed from a fixed position. Yang et al [22] evaluated the visibility of urban forests through a combination of field surveys and photography interpretation.

Methods

Results

Discussion

Conclusion