Numerical characteristics and spatial distribution of panoramic Street Green View index based on SegNet semantic segmentation in Savannah

Abstract

Panoramic green view index (PGVI) is an emerging index of urban greenery, which attracts researchers’ attention in recent years. It provides a quantitive method for reflecting real-life feelings about green space in urban areas. The PGVI needs to be calculated from massive datasets, which can be realized by artificial intelligence (AI) techniques. In this work, we used SegNet, an AI semantic segmentation tool, to distinguish urban elements, such as buildings, sky, and people. In total, 6874 panoramic street pictures with an interval of 10 m in the Savannah Historic District were used for the analysis of PGVI and its distribution. Results show that both the PGVI value and its distribution types can reflect the characteristics of regional green space. Good urban greenery can be distributed normally, which also provides a method for greenery classification. The crucial factors influencing PGVI are the trees. Dense low trees with big canopies have a very positive influence. In addition, the grade and width of the road, the parks, and squares along the street also have an impact on PGVI. In Savanah Historic District, the road width of nearly 10 m, and the location near parks and squares, can significantly increase the PGVI of streets.