Google Street View shows promise for virtual street tree surveys

Berland, Adam; Lange, Daniel A.

doi:10.1016/j.ufug.2016.11.006

Cited by 98 publications

(61 citation statements)

References 15 publications

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“…A very wide range of urban studies was based on street view images, with examples including studies on three-dimensional (3D) urban reconstructions [11][12][13], specific scene recognition [14], investigations of plant and animal species [15][16][17], route selection [8,18], perceived safety evaluation [19], data extraction on alcohol consumption [20], evaluation of the visual perception of streets [21,22], and so on. In particular, the visual perception of streets serves as a basis for urban landscape planning and residential quality of life [22].…”

Section: Introductionmentioning

confidence: 99%

Use of Tencent Street View Imagery for Visual Perception of Streets

Cheng

Chu

Zong

et al. 2017

IJGI

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Abstract:The visual perception of streets plays an important role in urban planning, and contributes to the quality of residents' lives. However, evaluation of the visual perception of streetscapes has been restricted by inadequate techniques and the availability of data sources. The emergence of street view services (Google Street View, Tencent Street View, etc.) has provided an enormous number of new images at street level, thus shattering the restrictions imposed by the limited availability of data sources for evaluating streetscapes. This study explored the possibility of analyzing the visual perception of an urban street based on Tencent Street View images, and led to the proposal of four indices for characterizing the visual perception of streets: salient region saturation, visual entropy, a green view index, and a sky-openness index. We selected the Jianye District of Nanjing City, China, as the study area, where Tencent Street View is available. The results of this experiment indicated that the four indices proposed in this work can effectively reflect the visual attributes of streets. Thus, the proposed indices could facilitate the assessment of urban landscapes based on visual perception. In summary, this study suggests a new type of data for landscape study, and provides a technique for automatic information acquisition to determine the visual perception of streets.

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Section: Introductionmentioning

confidence: 99%

Use of Tencent Street View Imagery for Visual Perception of Streets

Cheng

Chu

Zong

et al. 2017

IJGI

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“…Currently, the most encompassing of existing street-level data is Google Street View, and existing survey and monitoring efforts assessed with Street View datasets likely illustrate a precedent for future work involving three-dimensional datasets from mobile laser scanning and mobile photogrammetry. There have been examples of using Google Street View to ascertain overall greenness [69] and to collect certain street tree parameters like species identification [70]. However, the use of Street View has been somewhat limited by its two-dimensional point of view, as Berland and Lange [70] also determined that it was not suitable for acquiring measurements.…”

Section: Discussionmentioning

confidence: 99%

Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements

Roberts¹,

Koeser

Abd-Elrahman

et al. 2019

Forests

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Urban forests are often heavily populated by street trees along right-of-ways (ROW), and monitoring efforts can enhance municipal tree management. Terrestrial photogrammetric techniques have been used to measure tree biometry, but have typically used images from various angles around individual trees or forest plots to capture the entire stem while also utilizing local coordinate systems (i.e., non-georeferenced data). We proposed the mobile collection of georeferenced imagery along 100 m sections of urban roadway to create photogrammetric point cloud datasets suitable for measuring stem diameters and attaining positional x and y coordinates of street trees. In a comparison between stationary and mobile photogrammetry, diameter measurements of urban street trees (N = 88) showed a slightly lower error (RMSE = 8.02%) relative to non-mobile stem measurements (RMSE = 10.37%). Tree Y-coordinates throughout urban sites for mobile photogrammetric data showed a lower standard deviation of 1.70 m relative to 2.38 m for a handheld GPS, which was similar for X-coordinates where photogrammetry and handheld GPS coordinates showed standard deviations of 1.59 m and the handheld GPS 2.36 m, respectively—suggesting higher precision for the mobile photogrammetric models. The mobile photogrammetric system used in this study to create georeferenced models for measuring stem diameters and mapping tree positions can also be potentially expanded for more wide-scale applications related to tree inventory and monitoring of roadside infrastructure.

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“…The urban forest VGI data are a rich source of often unavailable data for policy practitioners [31], and can reduce some of the costs [50] associated with municipal field surveys, however, the question remains as to whether they actually being used. Interviews with local governments and urban forest stewardship groups could help to answer this important question.…”

Section: Discussionmentioning

confidence: 99%

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

2017

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Volunteered geographic information (VGI) has been heralded as a promising new data source for urban planning and policymaking. However, there are also concerns surrounding uneven levels of participation and spatial coverage, despite the promotion of VGI as a means to increase access to geographic knowledge production. To begin addressing these concerns, this research examines the spatial distribution and data richness of urban forest VGI in Philadelphia, Pennsylvania and San Francisco, California. Using ordinary least squares (OLS), general linear models (GLM), and spatial autoregressive models, our findings reveal that sociodemographic and environmental indicators are strong predictors of both densities of attributed trees and data richness. Although recent digital urban tree inventory applications present significant opportunities for collaborative data gathering, innovative research, and improved policymaking, asymmetries in the quantity and quality of the data may undermine their effectiveness. If these incomplete and uneven datasets are used in policymaking, environmental justice issues may arise.

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Google Street View shows promise for virtual street tree surveys

Cited by 98 publications

References 15 publications

Use of Tencent Street View Imagery for Visual Perception of Streets

Use of Tencent Street View Imagery for Visual Perception of Streets

Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements

Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

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