Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Ignatius, Manu; Xu, Rongqiao; Hou, Yujun; Liang, Xiaoling; Zhao, T.C.; Chen, S.; Wong, Nyuk Hien; Biljecki, Filip

doi:10.5194/isprs-annals-x-4-w2-2022-121-2022

Cited by 11 publications

(4 citation statements)

References 58 publications

(70 reference statements)

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“…The micro-scale built environment described by SVIs is also related to other indicators of residential behaviors, including walkability [52,53], bikeability [54][55][56], running [35], public transit ridership [57], and, therefore, mode choice [58,59] and active living [60][61][62]. Moreover, SVIs can infer the urban forms like street canyons and density [63,64] that explain local climate zones [65][66][67], an effective indicator for modeling neighborhood microclimate, outdoor comfort, and urban heat island effects [68][69][70], which ultimately influence energy usage and CEs.…”

Section: Hypothesis and Research Designmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Remote Sensing

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Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns.

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Section: Hypothesis and Research Designmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Remote Sensing

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“…The micro-scale built environment described by SVI is also related to other indicators of residential behaviors, including walkability (Ha et al, 2023), bikeability (Ito and Biljecki, 2021;Qiu and Chang, 2021;Song et al, 2023), running (Dong et al, 2023), public transit ridership (Su et al, 2022), therefore the mode choice (Koo et al, 2023;Wu, Yao and Wang, 2023) and active living (Sallis et al, 2006;Steinmetz-Wood et al, 2019). Moreover, SVI can infer the urban forms like street canyons and density (Middel et al, 2019;Qiu et al, 2021) that explains local climate zones (Cao et al, 2022;Ignatius et al, 2022;X. Xu, Qiu, Li, Huang, et al, 2022) -an effective indicator for modeling neighborhood microclimate, outdoor comfort, and urban heat island effects Oke, 2009, 2012;C.…”

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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“…Many previous and current studies focus on the city and building scales and could not show the dynamic interactions on the district scale with high-resolution 1 , 4 . Furthermore, urban climate studies have been focusing on the temperate climate zones, especially in North America and Europe, and there is a lack of such studies focusing on the tropical climate zones 12 – 14 .…”

Section: Background and Summarymentioning

confidence: 99%

District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images

Lin,

Ramani,

Martin

et al. 2023

Sci Data

Self Cite

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This paper describes a dataset collected by infrared thermography, a non-contact, non-intrusive technique to acquire data and analyze the built environment in various aspects. While most studies focus on the city and building scales, an observatory installed on a rooftop provides high temporal and spatial resolution observations with dynamic interactions on the district scale. The rooftop infrared thermography observatory with a multi-modal platform capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore. It was placed on the top of two buildings that overlook the outdoor context of the National University of Singapore campus. The platform collects remote sensing data from tropical areas on a temporal scale, allowing users to determine the temperature trend of individual features such as buildings, roads, and vegetation. The dataset includes 1,365,921 thermal images collected on average at approximately 10-second intervals from two locations during ten months.

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Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

Cited by 11 publications

References 58 publications

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emissions 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

District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images

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