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Open AccessArticle

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

by Xu Chen 1,2,3, Qingyan Meng 1,3,*, Die Hu 1,2,3, Linlin Zhang 1,2,3 and Jian Yang 1,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 101400, China
3
Sanya Institute of Remote Sensing, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Forests 2019, 10(12), 1109; https://doi.org/10.3390/f10121109
Received: 25 October 2019 / Revised: 1 December 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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-meter 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.
Keywords: Baidu panoramic street view; Panoramic Green View Index; street-side greenery; urban green spaces assessment Baidu panoramic street view; Panoramic Green View Index; street-side greenery; urban green spaces assessment
MDPI and ACS Style

Chen, X.; Meng, Q.; Hu, D.; Zhang, L.; Yang, J. Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index. Forests 2019, 10, 1109.

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