Numerical characteristics and spatial distribution of panoramic Street Green View index based on SegNet semantic segmentation in Savannah |
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| Institution: | 1. Department of Urban Planning, College of Architecture, Nanjing Tech University, Nanjing, China;2. Department of Landscape Architecture, Ecology School, Shanghai Institute of Technology, Shanghai, China;1. Landscape Design and Environmental Management Studio, Department of Plant and Soil Sciences, Faculty of Agriculture, Chiang Mai University, Thailand;2. Center of Excellence for Natural Disaster Management (CENDIM), Chiang Mai University, 239 Huay Kaew Road, Tumbol Suthep, Amphoe Mueang, Chiang Mai 50200, Thailand;3. Department of Architecture and Civil Engineering, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong 00000, Hong Kong,;4. Urban Environments and Human Health Lab, HKUrbanlabs, Faculty of Architecture, The University of Hong Kong, SAR, China;5. Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, SAR, China;6. National Center of Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 W Clark St, Urbana, IL 61801, United States;1. School of Economics, Ji Nan University, Guangzhou, 510632, China;2. School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China;3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China;4. Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong, China;5. School of Geography and Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China;6. Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China;7. Institute of Geography, School of GeoSciences, University of Edinburgh, Edinburgh, UK;1. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China;2. Agricultural and Biological Engineering Department / Tropical Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Homestead, FL 33031, USA;3. General Education Department, Taishan College of Science and Technology, Tai’an 271000, China;4. Dezhou Natural Resources Bureau & Forestry Bureau, Dezhou 25300, China;1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, 500 Dongchuan Rd., Shanghai 200241, China;2. School of Geographic Sciences, East China Normal University, 500 Dongchuan Rd., Shanghai 200241, China;3. Department of Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA;4. Shanghai Surveying and Mapping Institute, 419 Wuning Rd., Shanghai 200063, China |
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| 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. |
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| Keywords: | Green view index (GVI) Google street view(GSV) Green space Panorama pictures SegNet Savannah |
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