Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai

He, Yuhao; Zhao, Qianlong; Sun, Shanqi; Li, Wenjing; Qiu, Waishan

doi:10.3390/ijgi13040112

Cited by 2 publications

(2 citation statements)

References 50 publications

(76 reference statements)

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“…Meanwhile, SVI data are publicly available and frequently updated to capture groundlevel panorama street scenes [114]. SVIs are an ideal dataset to comprehensively describe the urban environmental variability [115] and citizen behaviors, including building height [116], streetscape features [117], green and water systems [118], land-use classification [94,119,120], openness [121], road networks [122], mobile monitoring [98], mobility patterns [123,124], sun-glare-related traffic crashes [125], land use [79,[126][127][128], and residential behavior [129].…”

Section: Svis For Urban Form Modelingmentioning

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: Svis For Urban Form Modelingmentioning

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

Self Cite

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“…Among them, a significant subset (280+ papers) has extensively utilized Street View Imagery (SVI) data [9] and artificial intelligence (AI) models, including machine learning (ML), deep learning (DL), and computer vision (CV) for urban-scale visual auditing [10][11][12]. Emerging studies [13][14][15][16] have indicated that street scene qualities significantly affect human behaviors, including running [17], walking [18], mental health [19][20][21], leisure activities [22], job and housing decisions [23,24], crime [25], and carbon emissions [26].…”

Section: Introduction 1public Space and Safety Perceptionmentioning

confidence: 99%

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

Liu,

Li,

Ren

et al. 2024

J. Imaging

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A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

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Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai

Cited by 2 publications

References 50 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

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

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