Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness
Abstract
:1. Introduction
1.1. Background
1.2. Objectives and Research Structure
2. Materials and Methods
2.1. Overview of the Analysis and Study Area
2.2. Data Description and Extraction
2.2.1. Mapillary Imagery Data
2.2.2. Reference Data for Accuracy Evaluation
2.3. Image Filtering Method
2.4. Accuracy Evaluation
3. Results
3.1. Image Classification Method
3.2. Accuracy Evaluation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Authors | Year | Study Area | Purpose | Imagery Data | Panorama | Data Collector |
---|---|---|---|---|---|---|
Yang et al. [21] | 2009 | Berkeley, United States | To develop the Green View Index (GVI) to evaluate the visibility of urban forests | - | No | researcher |
Li et al. [29] | 2015 | New York, United States | To propose a modified GVI formula using GSV images | GSV 1 | Yes | company |
Li et al. [50] | 2015 | Hartford, United States | To explore the distribution of street greenery and its association with residents’ socioeconomic conditions | GSV | Yes | company |
Long and Liu [51] | 2017 | 245 major Chinese cities | To propose an automatic method to determine street greenery and analyze the distribution of street greenery | TSV 2 | Yes | company |
Jiang et al. [18] | 2017 | The Midwestern United States | To assess associations among two remotely sensed and three eye-level tree cover density measures | - | Yes | researcher |
Seiferling et al. [52] | 2017 | New York and Boston, United States | To test a novel application of computer vision to quantify urban tree cover at the street-level | GSV | Yes | company |
Dong, Zhang, and Zhao [53] | 2018 | Beijing, China | To quantify street greenery in study area, analyze the relations with road parameters, and compare the visual greenery of different road types | TSV | Yes | company |
Lu, Sarkar, and Xiao [54] | 2018 | Hong Kong, China | To develop methods and tools to assess the availability of eye-level street greenery and investigate the effect of street-level greenery on walking behavior | GSV | Yes | company |
Villeneuve et al. [6] | 2018 | Ottawa, Canada | To assess associations between greenness, walkability, recreational physical activity, and health (comparing the NDVI with the GSV measure of vegetation) | GSV | Yes | company |
Zhang and Dong [55] | 2018 | Beijing, China | To investigate the impacts of street visible greenery on housing prices | TSV | Yes | company |
larkin and Hystad [19] | 2019 | Portland, United States | To evaluate GSV-based green space exposure measures as new approach for health research | GSV | Yes | company |
Lu [56] | 2019 | Hong Kong, China | To assess both the quantity and quality of street greenery and investigate the association between them and physical activity | GSV | Yes | company |
Yang et al. [57] | 2019 | Hong Kong, China | To examine the associations of urban greenery and older adults’ physical activity | GSV | Yes | company |
Ye et al. [58] | 2019 | Shanghai, China | To measure the potential economic effect of street greenery | BTV 3 | Yes | company |
Ye et al. [32] | 2019 | Singapore | To propose an approach for quantifying the daily exposure of urban residents to eye-level street greenery | GSV | Yes | company |
Chen et al. [59] | 2020 | Shenzhen, China | To explore the influence of greening factors on the use of shared bicycles | TSV | Yes | company |
Chen, Zhou, and Li [60] | 2020 | The Pearl River Delta Urban Agglomeration, China | To quantify green view values and explore their potentially influential factors | BTV | Yes | company |
Kumakoshi et al. [61] | 2020 | Yokohama, Japan | To propose an improved greenery visibility indicator (standardized GVI) and quantify the relation between sGVI and other green metrics | GSV | Yes | company |
Tong et al. [62] | 2020 | Nanjing, China | To assess street greenery using multiple indicators | TSV | Yes | company |
Wang et al. [63] | 2020 | Shenzhen, China | To explore the relationship between eye-level greenness and cycling behaviors | TSV | Yes | company |
Wu et al. [64] | 2020 | Beijing, China | To investigate the effect of street greenery on active travel considering road classification | BTV | Yes | company |
Zang et al. [65] | 2020 | Hong Kong, China | To explore the relationship between urban greenery and walking behaviors of older adults | BTV | Yes | company |
Ki and Lee [66] | 2021 | Seoul, Korea | To examine GVI (the difference with traditional greenery variables) and explore its associations with walking activities | GSV | Yes | company |
Li [67] | 2020 | New York, United States | To map and analyze the spatial distribution and temporal change in the GVI | GSV | Yes | company |
Xia et al. [68] | 2021 | Osaka, Japan | To develop a method to determine the greenery amount of street view images and propose the Panoramic View Green View Index for measuring the visible street-level greenery | GSV | Yes | company |
Yang et al. [69] | 2021 | Hong Kong, China | To examine the effects of streetscape greenery on the walking behavior of older adults | GSV | Yes | company |
Yang et al. [70] | 2021 | Hong Kong, China | To develop a novel method to assess both the quantity and quality of park greenery from eye-level photographs of parks and explore the associations with park usage | - | No | researcher |
Zhang, Tan, and Richards [71] | 2021 | Singapore | To examine the associations of different indicators of urban green spaces with health | GSV | Yes | company |
He et al. [72] | 2022 | Shanghai, China | To examine the complex relationship between urban density, urban greenery, and older people’s life satisfaction | BTV | Yes | company |
Xue et al. [73] | 2022 | Guangzhou, China | To introduce Visible Difference Vegetation Index for GVI calculation and explore the spatial distribution of street greenery in Guangzhou | BTV | Yes | company |
Appendix B
Image Classification Experiment
- Pictures of ordinary roads;
- Pictures with the front view parallel with the street segment and taken at the horizontal level;
- Pictures taken in an ideal environment for streetscape elements observation.
References
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Features | Description | Extraction Method |
---|---|---|
1. Image elements (the ratio of roads, sidewalks, buildings, walls, fences, sky, riders, cars, buses, trains, motorcycles, and bicycles within the images) | To distinguish the photographing place To recognize obstacles | PSPNet model trained by Cityscapes |
2. VP position (vertical and horizontal distance to the center point of the image) | To ensure images are level with the horizontal position and aimed at the road direction | 3-line RANSAC VP detection model |
3. Brightness (brightness value) | To avoid pictures taken in low-light situations such as night and rainy days | OpenCV |
4. Clarity (clarity value) | To recognize blurry pictures | OpenCV |
Predicted Class | |||
---|---|---|---|
Suitable | Unsuitable | ||
Actual class | Suitable | 33 | 9 |
Unsuitable | 3 | 51 |
1 | 2 | 3 | ||
---|---|---|---|---|
1 | Values based on the original Mapillary dataset (n = 114) | 1 | ||
2 | Values based on the filtered Mapillary dataset (n= 75) | 0.919 ** | 1 | |
3 | Reference values (n = 114) | 0.740 ** | 0.829 ** | 1 |
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Zheng, X.; Amemiya, M. Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness. ISPRS Int. J. Geo-Inf. 2023, 12, 108. https://doi.org/10.3390/ijgi12030108
Zheng X, Amemiya M. Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness. ISPRS International Journal of Geo-Information. 2023; 12(3):108. https://doi.org/10.3390/ijgi12030108
Chicago/Turabian StyleZheng, Xinrui, and Mamoru Amemiya. 2023. "Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness" ISPRS International Journal of Geo-Information 12, no. 3: 108. https://doi.org/10.3390/ijgi12030108
APA StyleZheng, X., & Amemiya, M. (2023). Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness. ISPRS International Journal of Geo-Information, 12(3), 108. https://doi.org/10.3390/ijgi12030108