Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing

Tang, Jingxian; Long, Ying

doi:10.1016/j.landurbplan.2018.09.015

Cited by 220 publications

(140 citation statements)

References 34 publications

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“…Features characterizing the streetscape have been proposed, such as streetscape skeleton variables and streetscape or scene elements, among which important features include enclosure, openness, and greenery [12,14,15]. Enclosure is the property of a contained street space with room-like proportions related to the amount of building facades on each side of the street.…”

Section: Measurement Of Streetscape Features As a Reflection Of Urbanmentioning

confidence: 99%

“…The advent of street view images from Google StreetView and similar online data services has opened the door of opportunities to remedy this disadvantage. A series of recent studies managed to extract streetscape features from SVIs and applied them in a range of applications, including the perception and quality of the urban environment [15,[17][18][19][20], street livability and walkability [21][22][23][24], environmental audit for human health and wellbeing [25][26][27][28][29], urban inequality and socioeconomic changes of neighborhoods [30][31][32][33][34], urban safety [35,36], and information retrieval for adjacent land uses [37]. Among these studies, many assume implicitly or explicitly that the extracted streetscape features, representing the physical appearance of streets, from SVIs can reflect place-related functions that serve human activities both on the street and those associated with the uses of buildings on the sides [23,30,[32][33][34]37].…”

Section: Measurement Of Streetscape Features As a Reflection Of Urbanmentioning

confidence: 99%

“…In order to reflect the place-related function of the streetscape, we clustered the streetscape of grids into classes based on the streetscape features extracted from SVIs. According to existing research [15,17,19], using the three dimensions of enclosure, openness, and greenery features is sufficient to portray the spatial characteristics of streets, while keeping the model as easy to interpret and analyze. In order to support the rule-based classification, we used information entropy [40] to measure the proportional relationship between these streetscape elements.…”

Section: Classifying Streetscapementioning

confidence: 99%

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Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Zi-tong

Kan

et al. 2019

Sustainability

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Streets, as one type of land use, are generally treated as developed or impervious areas in most of the land-use/land-cover studies. This coarse classification substantially understates the value of streets as a type of public space with the most complexity. Street space, being an important arena for urban vitality, is valued by various dimensions, such as transportation, recreation, aesthetics, public health, and social interactions. Traditional remote sensing approaches taking a sky viewpoint cannot capture these dimensions not only due to the resolution issue but also the lack of a citizen viewpoint. The proliferation of street view images provides an unprecedented opportunity to characterize street spaces from a citizen perspective at the human scale for an entire city. This paper aims to characterize and classify street spaces based on features extracted from street view images by a deep learning model of computer vision. A rule-based clustering method is devised to support the empirically generated classification of street spaces. The proposed classification scheme of street spaces can serve as an indirect indicator of place-related functions if not a direct one, once its relationship with urban functions is empirically tested and established. This approach is empirically applied to Beijing city to demonstrate its validity.

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Section: Measurement Of Streetscape Features As a Reflection Of Urbanmentioning

confidence: 99%

Section: Measurement Of Streetscape Features As a Reflection Of Urbanmentioning

confidence: 99%

Section: Classifying Streetscapementioning

confidence: 99%

See 1 more Smart Citation

Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Zi-tong

Kan

et al. 2019

Sustainability

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“…The first one is based on spatial data analyses, which describe the landscape compositions and patterns in the form of 2D categorical maps [6,7]. The second one is based on eye-level photographs, and the landscape is characterized either by qualitative questionnaire surveys [8][9][10][11] or quantitative computer vision and machine learning algorithms [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…The issue related to the composition and unstandardized FOV can be resolved with the use of 360 • panoramic images, which capture the whole scene surrounding the observer. Huge progress in computer vision and machine learning algorithms observed lately allowed for observer-independent image classification, which can be applied for landscape description [13,30,31]. Panoramic images from the Google Street View (GSV) database along with machine learning has proved to be useful for quantification of the landscape in an urban environment [32][33][34][35]; however, GSV image locations are biased towards streets and do not offer complete coverage of open areas at this point.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.

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Human-scale Urban Form and Its Application in DAD

Long

Zhang

2020

Strategies for Sustainability

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Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing

Cited by 220 publications

References 34 publications

Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Human-scale Urban Form and Its Application in DAD

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