A novel method for predicting and mapping the occurrence of sun glare using Google Street View

Li, Xiaojiang; Cai, Bill; Qiu, Waishan; Zhao, Jinhua; Ratti, Carlo

doi:10.1016/j.trc.2019.07.013

Cited by 35 publications

(16 citation statements)

References 8 publications

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“…Additionally, crowdsourcing, computer vision (CV) and machine learning (ML) technologies have also proved their accuracy and efficiency for large-scale application (Naik et al, 2016(Naik et al, , 2014. In particular, new studies within this regard have objectively detected curb ramps (Hara et al, 2014), measured eye-level greenery view index (GVI) (Li et al, 2015), counted pedestrian numbers (Yin et al, 2015), and predicted sun glare (Li et al, 2019).…”

Section: Eye-level Street Perceptionsmentioning

confidence: 99%

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Qiu¹,

Li²,

Zhang³

et al. 2021

Preprint

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The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

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Section: Eye-level Street Perceptionsmentioning

confidence: 99%

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Qiu¹,

Li²,

Zhang³

et al. 2021

Preprint

Self Cite

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“…The PSPNet can segment the street-level images into 150 categories, such as trees, buildings, pavement, and sky. The PSPNet is the state-of-the-art street-level image segmentation algorithm with an overall pixel-level segmentation accuracy higher than 80% for complex visual scenes (Li et al., 2019; Zhao et al., 2017) while the segmentation accuracy on greenery can be as high as 95%. Figure 3 shows the image segmentation on three street-level images for street tree canopy extraction.…”

Section: Methodsmentioning

confidence: 99%

Examining the spatial distribution and temporal change of the green view index in New York City using Google Street View images and deep learning

2020

Environment and Planning B: Urban Analytics and City Science

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As an important part of the urban ecosystem, urban trees provide various benefits to urban residents. It is therefore important to examine the spatial distribution and the temporal change in urban tree canopies. Different from traditional overhead view remote sensing-based methods, street-level images, which present the most common view that people have of greenery, provide a more human-centric way to quantify street tree canopies. This study mapped and analyzed the spatial distribution and temporal change in the green view index, which represents the visibility of tree canopies along streets in New York City during the last 10 years using historical Google Street View images. Deep learning and computer vision algorithms were used to derive the quantitative information of street tree canopies from street-level images and map the spatial distribution of the green view index. This study further investigated the potential disparities in terms of green view index across different racial/ethnic groups by comparing with census data. Results show that non-Hispanic Whites tend to live in neighborhoods with higher green view index and Hispanics tend to live in neighborhoods with lower green view index. The green view index values in New York City have increased slightly in the last 10 years, and the change of green view index has no significant correlation with resident’s ethnic/racial status. This study proves the usability of historical Google Street View images for monitoring the temporal change of urban street tree canopy changes at large scale, and it also provides insights and a valuable reference for urban greening programs.

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“…Based on research on autonomous or non-autonomous cars, on different communication technologies, on decentralized and centralized connectivity [45,46], we have concluded that there is a rich cluster of information and interaction produced between vehicles and other road entities. In the Solution section, we are going to explore a dynamic visualization [47] of road information in terms of predictive weather conditions [48], visibility [49,50] and outdoor illumination [51,52] and potential sun glares [53,54] at the future moment of passing through that area, with markings on possible hazards [55,56], for a better trip planning. We chose this kind of information compared to the better-known real-time crowd sourced [57] traffic data, as an example of a novel cluster of visualized road information.…”

Section: Connected Cars and Smart Transport Infrastructurementioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

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A novel method for predicting and mapping the occurrence of sun glare using Google Street View

Cited by 35 publications

References 8 publications

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai

Examining the spatial distribution and temporal change of the green view index in New York City using Google Street View images and deep learning

Ubiquitous Computing: Driving in the Intelligent Environment

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