The Validity of Street View Service Applied to Ambiance Perception of Street: A Comparison of Assessment in Real Site and Baidu Street View

Feng, Guang; Zou, Guangtian; Piga, Barbara Ester Adele; Hu, Haihui

doi:10.1007/978-3-030-80829-7_91

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Furthermore, the subjective perception measures based on the real environment and based on the street view may not be consistent. The study from Feng and others ([ 59 ]) points out that the Baidu SVPs and the real site are relatively consistent in the subjective perception assessment of street quality while significant differences exist in the ambiance perception. Yue and others ([ 60 ]) also acknowledge the importance of human visual perception of the urban environment, in addition to the physical environment.…”

Section: Discussionmentioning

confidence: 99%

The influence of neighborhood quality on tourism in China: Using Baidu Street View pictures and deep learning techniques

Chen

Lin

2022

PLoS ONE

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Previous studies have investigated the determinants of urban tourism development from the various attributes of neighborhood quality. However, traditional methods to assess neighborhood quality are often subjective, costly, and only on a small scale. To fill this research gap, this study applies the recent development in big data of street view images, deep learning algorithms, and image processing technology to assess quantitatively four attributes of neighborhood quality, namely street facilities, architectural landscape, green or ecological environment, and scene visibility. The paper collects more than 7.8 million Baidu SVPs of 232 prefecture-level cities in China and applies deep learning techniques to recognize these images. This paper then tries to examine the influence of neighborhood quality on regional tourism development. Empirical results show that both levels of street facilities and greenery environment promote tourism. However, the construction intensity of the landscape has an inhibitory influence on the development of tourism. The threshold test shows that the intensity of the influence varies with the city’s overall economic level. These conclusions are of great significance for the development of China’s urban construction and tourism economy, and also provide a useful reference for policymakers. The methodological procedure is reduplicative and can be applied to other challenging cases.

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Section: Discussionmentioning

confidence: 99%

The influence of neighborhood quality on tourism in China: Using Baidu Street View pictures and deep learning techniques

Chen

Lin

2022

PLoS ONE

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“…However, the same remark cannot be easily extended to the tools that directly predict a perceptual quality from an input image of the urban space. As mentioned in (Feng et al, 2021, the panoramic images should be used as a whole to accurately predict individual reactions, which is not possible with the trained networks and datasets available. Thus, the only method currently available to characterize a place is to break it down into smaller pieces and analyze each one of them.…”

Section: Splitting the Panoramas For Automatic Processingmentioning

confidence: 99%

“…Some studies have demonstrated that to some extent, these services can be used as a replacement tool to evaluate the perception of the urban space (Rundle et al, 2011, Kelly et al, 2013. For instance, (Feng et al, 2021) showed that GSV can be used to assess the subjective perception of the urban environment, but is not reliable enough to assess the overall atmosphere. One feature of these services that make this assessment possible is the presence of 360°images, which offer a panoramic and immersive view to the users who can see the surrounding urban space as a whole, compared to traditional pictures with * Corresponding author a limited and arbitrarily oriented field of view (FOV).…”

Section: Introduction 11 360°images To Evaluate Visual Urban Perceptionmentioning

confidence: 99%

“…One feature of these services that make this assessment possible is the presence of 360°images, which offer a panoramic and immersive view to the users who can see the surrounding urban space as a whole, compared to traditional pictures with * Corresponding author a limited and arbitrarily oriented field of view (FOV). This is especially important since the subjective reaction to the urban space is not just a sum of perceptual responses to stimuli in the field of view of the pedestrian, but a global response to their surroundings (Feng et al, 2021). Therefore, the whole spherical view is important to assess the perception of the urban space since even turning around can heavily influence the user's reaction (Piga et al, 2021), the off-screen being a part of the scenery.…”

Section: Introduction 11 360°images To Evaluate Visual Urban Perceptionmentioning

confidence: 99%

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The Whole Is Other Than the Sum of Its Parts: Sensibility Analysis of 360° Urban Image Splitting

Beaucamp

Leduc

Tourre

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained neural networks. The sub-images cover the 360° field of view, e.g. front, back, left, and right views. The four scores obtained are averaged to represent the level of the quality at the location of the panorama. However, this split introduces a bias since some elements of the urban space are halved over two images and the global context is lost. Based on the Place Pulse 2.0 dataset, this paper investigates the impact of splitting 360° panoramas on the perceptual scores predicted by neural networks. For each panorama, we predict the score for thirty-six overlapping sub-images. The scores were shown to have high variability and be highly dependent on the direction of the camera for the perspective images. This indicates that four images are not sufficient to capture the complexity of the perceptual qualities of the urban space.

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“…How to accurately identify traffic black spots on the basis of existing data has not yet been uniformly determined. In previous studies, traffic accident data have mostly come from the historical accident records of the traffic management department, and the number of traffic accidents is analyzed to determine the locations of the traffic black spots by using mathematical and statistical methods [8][9][10]. The data are relatively singular and not easy to collect and manage, and more importantly the number of accident features is small, which does not allow for a specific analysis of the causes of traffic black spots.…”

Section: Introductionmentioning

confidence: 99%

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Liu,

Wang,

et al. 2024

Applied Sciences

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Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

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The Validity of Street View Service Applied to Ambiance Perception of Street: A Comparison of Assessment in Real Site and Baidu Street View

Cited by 3 publications

References 19 publications

The influence of neighborhood quality on tourism in China: Using Baidu Street View pictures and deep learning techniques

The influence of neighborhood quality on tourism in China: Using Baidu Street View pictures and deep learning techniques

The Whole Is Other Than the Sum of Its Parts: Sensibility Analysis of 360° Urban Image Splitting

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

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