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.
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