Revealing spatio-temporal evolution of urban visual environments with street view imagery

Liang, Xiucheng; Zhao, Tianhong; Biljecki, Filip

doi:10.1016/j.landurbplan.2023.104802

Cited by 42 publications

(8 citation statements)

References 97 publications

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“…CV methods in urban sensing can collect various environmental and socioeconomic data from vast collections of images and videos [ 129 ]. Understanding visual environments entails more than just identifying physical objects and environmental attributes; it also encompasses human experiences and perceptions [ 130 ]. Song et al.…”

Section: Results: Analysis and Synthesismentioning

confidence: 99%

“…AI has enabled urban researchers, designers, and planners with extensive proficiency in utilizing CV for designing, observing, and modeling urban environments and planning, evaluation processes, and stakeholder participation (Wael et al., 2022). Visual data are of critical importance in the process of urban planning and design, thereby the significance of CV [ 130 , 136 ] as a valuable tool for decision-making and planning processes [ 136 ]. In particular, CV holds significant promise in its ability to provide accessible and cost-effective tools for urban assessment and modeling [ 137 , 138 ].…”

Section: Results: Analysis and Synthesismentioning

confidence: 99%

See 1 more Smart Citation

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

Bibri,

Huang,

Jagatheesaperumal

et al. 2024

Environmental Science and Ecotechnology

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Section: Results: Analysis and Synthesismentioning

confidence: 99%

Section: Results: Analysis and Synthesismentioning

confidence: 99%

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

Bibri,

Huang,

Jagatheesaperumal

et al. 2024

Environmental Science and Ecotechnology

View full text Add to dashboard Cite

“…In the 1960s, Cullen developed the theory of "Serial Vision", drawing a series of street scenes at predetermined angles and directions along viewing routes to present the visual sequences of streets [70]. Currently, many studies predefine and fix observation directions and angles on a street to collect street view images according to people's observation habits [54,69,71,72]. Based on this, and considering the planning intentions of the renewal team and the preferences of most visitors, this study identified four main viewing and touring routes for the area, along with the corresponding viewing directions (since the northeastern area of Xiaoxihu is still under construction and closed to the public, we did not collect images there).…”

Section: Photographymentioning

confidence: 99%

A Method for Measuring the Visual Coherence of Buildings in Residential Historic Areas: A Case Study of the Xiaoxihu Historic Area in Nanjing, China

Xu,

Pan

2024

Buildings

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Residential historic areas are currently the main focus of urban renewal efforts in China, primarily consisting of traditional residential buildings with similar characteristics. Hence, the visual coherence of buildings (VCoB) plays a crucial role in such areas, not only pertaining to the visual quality of the urban landscape but also regarding the preservation of historic features. The accurate measurement of the VCoB is a prerequisite for undertaking optimization efforts. However, discussions of the VCoB in the built environment are limited and seldom address residential historic areas, and methods for measuring the VCoB have yet to be refined. Therefore, taking the Xiaoxihu Historic Area in Nanjing as a case study, this work aimed to develop a more refined method for measuring the VCoB in residential historic areas based on objective physical features. The method is mainly based on the human-level perspective, identifying three key visual elements within this perspective as the objects of measurement. It collected visual data through photography, with deep learning and computer graphics processing tools being utilized to identify and extract the visual elements. Then, the study established a corresponding framework of indicators for different visual elements and optimizes the methods for indicator calculation. Through an assessment involving professionals, we validated the high accuracy of the measurement method proposed in this study. Furthermore, the study discusses factors affecting the VCoB, methods to enhance the VCoB, and the required degree of the VCoB based on the results of the measurement. The method developed in this research will provide support for the visual analysis of the urban built environment and urban renewal practices.

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“…We looked through the street-view history in Baidu Map, which make it possible that users could see how a place has changed over the years and help on identifying changes in the physical environment, and there were few major construction projects in the study area during this period. Considering the street environment is rather stable in a short term (Liang, Zhao and Biljecki, 2023), we were able to assume no significant changes happened between the sample period (2019-2021). Notably, the SVI retrieval process is also consistent with all parameters including the heading, the position coordinates (longitude and latitude), the image resolution (width and height), the horizontal field, and the pitch.…”

Section: Svi Data Collectionmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

show abstract

Revealing spatio-temporal evolution of urban visual environments with street view imagery

Cited by 42 publications

References 97 publications

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

A Method for Measuring the Visual Coherence of Buildings in Residential Historic Areas: A Case Study of the Xiaoxihu Historic Area in Nanjing, China

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

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