2019
DOI: 10.1016/j.trc.2019.07.013
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A novel method for predicting and mapping the occurrence of sun glare using Google Street View

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Cited by 35 publications
(16 citation statements)
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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%
“…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%
“…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%
“…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%