2020
DOI: 10.1177/2399808320962511
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Examining the spatial distribution and temporal change of the green view index in New York City using Google Street View images and deep learning

Abstract: As an important part of the urban ecosystem, urban trees provide various benefits to urban residents. It is therefore important to examine the spatial distribution and the temporal change in urban tree canopies. Different from traditional overhead view remote sensing-based methods, street-level images, which present the most common view that people have of greenery, provide a more human-centric way to quantify street tree canopies. This study mapped and analyzed the spatial distribution and temporal change in … Show more

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Cited by 36 publications
(23 citation statements)
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“…In the present study we collected the nearest geo-tagged GSV images that were within 50 feet of each adolescent’s home address using the GSV image API [ 52 ]. The deep convolutional neural network PSPNet, trained based on ADE20K, was used to extract street greenery from the street-level images, where accuracy for the identification of greenery typically approaches 95% [ 53 , 54 ]. Based on the resulting image segmentation, we generate the green view index (GVI), which is calculated as follows: where the Area ri is the green (i.e., tree, shrub) pixel number in one of the six pictures taken in six different directions and Area ti is the number of total pixels in one of the six images [ 55 ].…”
Section: Methodsmentioning
confidence: 99%
“…In the present study we collected the nearest geo-tagged GSV images that were within 50 feet of each adolescent’s home address using the GSV image API [ 52 ]. The deep convolutional neural network PSPNet, trained based on ADE20K, was used to extract street greenery from the street-level images, where accuracy for the identification of greenery typically approaches 95% [ 53 , 54 ]. Based on the resulting image segmentation, we generate the green view index (GVI), which is calculated as follows: where the Area ri is the green (i.e., tree, shrub) pixel number in one of the six pictures taken in six different directions and Area ti is the number of total pixels in one of the six images [ 55 ].…”
Section: Methodsmentioning
confidence: 99%
“…Those studies suggested that aesthetics and well-kept streets might encourage people to walk for health benefits, which seemed to be intuitive. Observations of greenery and walkability by automated methods through GSV could also reflect social inequality between areas (Ki & Lee, 2021;Li, 2021;Li, Zhang, Li, Kuzovkina, & Weiner, 2015), and be positively related with the vitality of neighborhood organizations (e.g., sports clubs) and strength of social networks in local communities (Wang & Vermeulen, 2021).…”
Section: Investigation On Physical Environment Using Google Street Viewmentioning
confidence: 99%
“…2 The API needed location coordinates to display the GSV imagery nearby. If the purpose of collecting GSV images is to capture comprehensive streetscapes of target regions regardless of the existence of houses, referring to coordinates spotted at a certain interval on the API would be a common procedure (e.g., Jiang et al, 2020;Ki & Lee, 2021;Li, 2021;Li et al, 2018;Li, Zhang, Li, Kuzovkina, & Weiner, 2015;Zhang et al, 2018). On the other hand, if the purpose of image collection is to observe streetscapes around certain buildings or facilities, using their addresses or coordinates would be a more reasonable procedure (e.g., Wang & Vermeulen, 2021).…”
Section: Google Street View Imagery Collectionmentioning
confidence: 99%
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“…Panoramic images, such as those available from Google Street View (GSV), have become one of the most important sources for estimating the GVI, with several examples available [28,32,49,[51][52][53]. In these cases, the GVI is typically not estimated from four non-overlapping images (as in [44]), but rather from a set of segments of the 360 degree panoramic image, with slightly varying implementations.…”
Section: Green View Index (Gvi) On the Street Levelmentioning
confidence: 99%