Assessing urban greenery by harvesting street view data: A review

Lu, Yanzhi; Ferranti, Emma; Chapman, Lee; Pfrang, Christian

doi:10.1016/j.ufug.2023.127917

Cited by 22 publications

(8 citation statements)

References 107 publications

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“…Notably, most of the factors can be extracted from SVIs. For example, the green view index is a proxy of the greenery (Lu et al, 2023) which is important to carbon sequestration (Dwyer et al, 2000;Nowak and Crane, 2002;Birge et al, 2019), while the building view index is a proxy to building density and building height (Carrasco-Hernandez, Smedley and Webb, 2015;Gong et al, 2018) that significantly affect CE (Resch et al, 2016). The adequate public infrastructure and convenient transportation (e.g., road, streetlights, bus stop) may suggest a more walkable and bikeable neighborhood whose residents would have higher tendency for active travel (Li and Joh, 2017;Dong et al, 2023), resulting in lower CE (Zhang et al, 2020).…”

Section: Knowledge Gapmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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Section: Knowledge Gapmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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“…In contrast to the viewshed-based approach, made significant advances in this field by utilising automatically extracted Google Street View (GSV) images and employing image segmentation techniques for automating the greenery calculation process. This breakthrough has led to the emergence of several innovative computational approaches that have demonstrated a high level of agreement with human perception (Aikoh et al, 2023;Suppakittpaisarn et al, 2022;Torkko et al, 2023), offering several benefits such as reduced research time and workloads, increased accessibility, and the ability to study urban greenery without the need for physical visits to field sites (Lu et al, 2023;Rangel et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Accessing Eye-level Greenness Visibility from Open-Source Street View Images: A methodological development and implementation in multi-city and multi-country contexts

Sánchez,

Labib

2023

Preprint

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The urban natural environment provides numerous benefits, including augmenting the aesthetic appeal of urban landscapes and improving mental well-being. While diverse methods have been used to evaluate urban greenery, the assessment of eye-level greenness visibility using street-view level images is emerging due to its greater compatibility with human perception. Many existing studies predominantly rely on proprietary street view images provider such as Google Street View (GSV) data; the usage restrictions and lack of alignment with FAIR (Findability, Accessibility, Interoperability, and Reusability) principles present challenges in using proprietary images at scale. Therefore, incorporating Volunteered Street View Imagery (VSVI) platforms, such as Mapillary, is emerging as a promising alternative. In this study, we present a scalable and reproducible methodological framework for utilising Mapillary images for Green View Index (GVI) assessment using image segmentation approach and evaluate the completeness and usefulness of such data in diverse geographical contexts, including eleven cities (i.e., Amsterdam, Barcelona, Buenos Aires, City of Melbourne, Dhaka, Ho Chi Minh, Kampala, Kobe, Mexico City, Seattle, and Tel Aviv). We also evaluate the use of globally available satellite-based vegetation indices (e.g., Normalised Difference Vegetation Index-NDVI) to estimate GVI in locations where street-view images are unavailable. Our approach demonstrates the applicability of Mapillary data for GVI assessments, although revelling considerable disparities in image availability and usability between cities located in developed and developing countries. We also identified that the NDVI could be used effectively to estimate GVI values in locations where direct street-level imagery is limited. Additionally, the analysis reveals notable differences in greenness visibility across cities, particularly in high-density, lower-income cities in Africa and South Asia, compared to low-density, high-income cities in the USA and Europe.

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“…Automatic assignment of land cover in a city is a complex task, requiring additional databases that take into account time variation and spatial uncertainty, due to the high variability and coexistence of green infrastructure with other forms of land cover (e.g., trees along streets). Methods for determining tree parameters often use artificial intelligence in combination with one or more data sources [14], such as satellite imagery and light detection and ranging (LiDAR) data sets [15], neural networks to automatically classify forest LiDAR point cloud [16], UAVs, Google street view [17,18], or images taken with smartphones [12].…”

Section: Introductionmentioning

confidence: 99%

Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

Uciechowska-Grakowicz,

Herrera-Granados,

Biernat

et al. 2023

Remote Sensing

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The cities of the future should not only be smart, but also smart green, for the well-being of their inhabitants, the biodiversity of their ecosystems and for greater resilience to climate change. In a smart green city, the location of urban green spaces should be based on an analysis of the ecosystem services they provide. Therefore, it is necessary to develop appropriate information technology tools that process data from different sources to support the decision-making process by analysing ecosystem services. This article presents the methodology used to develop an urban green space planning tool, including its main challenges and solutions. Based on the integration of data from ALS, CLMS, topographic data, and orthoimagery, an urban green cover model and a 3D tree model were generated to complement a smart-city model with comprehensive statistics. The applied computational algorithms allow for reports on canopy volume, CO2 reduction, air pollutants, the effect of greenery on average temperature, interception, precipitation absorption, and changes in biomass. Furthermore, the tool can be used to analyse potential opportunities to modify the location of urban green spaces and their impact on ecosystem services. It can also assist urban planners in their decision-making process.

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Assessing urban greenery by harvesting street view data: A review

Cited by 22 publications

References 107 publications

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Accessing Eye-level Greenness Visibility from Open-Source Street View Images: A methodological development and implementation in multi-city and multi-country contexts

Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

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