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Google Street View와 딥러닝을 활용한 서울시 녹지 형평성 분석 -NDVI와 가로이미지 기반 녹지 산출방법과의 비교-

Title
Google Street View와 딥러닝을 활용한 서울시 녹지 형평성 분석 -NDVI와 가로이미지 기반 녹지 산출방법과의 비교-
Other Titles
Analysis of the Green Equity Using Google Street View and Deep Learning in Seoul, Korea : Focused on the Comparison between NDVIand Street Image-Based Green Calculation Method
Author
이수기
Keywords
녹지 형평성; 가로녹시율; 구글 가로 이미지; 딥러닝; 의미론적 분할; Green Equity; Green View Index; Google Street View; Deep Learning; Semantic Segmentation
Issue Date
2020-04
Publisher
대한국토·도시계획학회
Citation
국토계획, v. 56, no. 4, page. 194-211
Abstract
Urban green has various benefits, including promoting physical activity, improving residents’ health, and mitigating urban heat islands. Hence, urban green is considered essential for urban residents, but green inequity issues are being raised. Although several studies have analyzed green equity with the traditional measurement method, the conventional approach is limited in its inability to reflect the actual degree of the green exposure of residents. To fill this gap, this study aims to identify the actual green equity using the Green View Index (GVI), which can represent actual green exposure. This study utilized Google Street View (GSV) and computer vision techniques to measure the GVI. The normalized difference vegetation index (NDVI) and geographic information system (GIS) based green area variables, which are traditional green area variables, were used to compare these distributions with GVI. Furthermore, this study identified the degree of green equity through the relationship between the distribution of green variables and the vulnerable groups. In terms of statistical model, the spatial lag and spatial error models were used to control the spatial autocorrelation. The results of this study are as follows. First, there were significant distributional differences between traditional green variables and GVI. Specifically, traditional green variables were high in the periphery of Seoul. GVI, however, was shown as cold-spots in these areas and highly concentrated in Gangnam, Seocho, and Songpa-gu. Second, the GVI model showed a lack of street greenery where numerous vulnerable people live, unlike traditional green variable models. Specifically, low-income people tend to live in neighborhoods with less street vegetation. Therefore, the government should implement green supply policies for these target neighborhoods. Regarding the methodological perspective, the results indicate that the degree of green inequality may vary depending on the green measurement methods. Moreover, plans for the supply of green should be based on GVI that can represent the actual degree of the exposure of residents.
URI
http://kpaj.or.kr/_common/do.php?a=full&bidx=2679&aidx=30327https://repository.hanyang.ac.kr/handle/20.500.11754/165468
ISSN
1226-7147; 2383-9171
DOI
10.17208/jkpa.2021.08.56.4.194
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > URBAN PLANNING AND ENGINEERING(도시공학과) > Articles
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