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Study on the identification and dynamics of green vision rate in Jing’an district, Shanghai based on deeplab V3 + model


Street greening is an important part of urban landscape, and Green view index (GVI) has an important influence on the comfort of street space environment. In this paper, we analyze the GVI of street space based on big data of street view photos and neural network algorithm. By collecting about 25,000 street view photos from Tencent map, we synthesize the panoramic projection photos of equal area. The image segmentation is achieved by training models through DeeplabV3 + network framework, and then the GVI after image recognition is calculated by OpenCV. The spatial distribution of GVI on the street space is analyzed by means of GIS and other means. In this paper, four spatial regression models are applied to analyze the influencing factors of GVI in the context of urbanization development from three perspectives: historical planning, economic and social. A negative correlation between road class and GVI distribution was obtained. When analyzing the influence of different POI distributions on GVI, it was found that the distribution of hospitals and subway stations was significantly correlated with GVI, and the presence of hospitals in neighborhoods would increase the GVI of surrounding streets. The presence of subway stations, on the other hand, decreases the green visibility of the surrounding streets. This study contributes to the development of human-centered planning and design and provides scientific guidance for the construction and renovation of green space in streets by targeting and optimizing the distribution of specific social facilities in the area.

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Correspondence to Jiangbo Wang.

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Gou, A., Zhang, C. & Wang, J. Study on the identification and dynamics of green vision rate in Jing’an district, Shanghai based on deeplab V3 + model. Earth Sci Inform (2021).

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  • Spatial model
  • Green view index
  • Neural network
  • Jing’an District