The economic value of urban landscapes in a suburban city of Tokyo, Japan: A semantic segmentation approach using Google Street View images

Suzuki, Masatomo; Mori, Junichiro; Maeda, Takashi; Ikeda, Jun

doi:10.1080/13467581.2022.2070492

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Applications for semantic image segmentation include land use and land cover categorisation, colon crypt segmentation, and road sign detection [24][25][26]. Gonghu H et al extracted the proportion of streetscape elements from GSVP images by semantic segmentation and performed correlation and regression analyses between the VIWP rated values and the streetscape elements; Masatomo Suzuki et al looked into the connection between property values and the cityscape in residential low-rise buildings' neighbourhoods in suburban Tokyo, Japan, using Google Street View photo semantic segmentation [27]. Laura Martinez-sanchez et al trained and analysed skyline images by the semantic segmentation method and concluded that changes in the landscape's skyline photographs are applicable for estimating the distance to trees that are far off from trees on the horizon [28].…”

Section: Of 19mentioning

confidence: 99%

A Study on the Regional Evolution of the Hefei Political and Cultural New District Skyline Based on Exploratory Factor Analysis and Semantic Segmentation

Zhang,

Yuan

et al. 2024

Buildings

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The general population’s assessment and perception of a city can be influenced by its skyline, which is one of its representatives. This paper uses semantic segmentation and exploratory factor analysis to conduct a study from two different perspectives, aiming to analyse the development and current situation of the skyline of Hefei’s political and cultural new district. By collecting pictures of the skyline of the New Administrative and Cultural District, the New Administrative and Cultural District of Hefei’s skyline is being studied for overall changes using the semantic segmentation method, and to evaluate the quality of the skyline in various years, the entropy weight–TOPSIS is employed. Through the literature investigation and creation status of the new political and cultural district, the index system is scientifically formulated to conduct a questionnaire survey, and its reliability and validity are tested. This study is conducted with exploratory factor analysis of factors that are vital in the city skyline. The results of this study show that (1) after the semantic segmentation of the pictures, it is found that the proportion of buildings and the proportion of vegetation are both rising; the proportion of buildings tends to level off after 2018, and the proportion of vegetation shows a uniform increase. In the relative proximity ranking, the overall trend is increasing, and the skyline is ranked first in 2024. (2) Two variables were identified based on the findings of the exploratory factor analysis: ‘skyline value’ and ‘neighbourhood ecological quality’. The results of this study show that the skyline of Hefei’s New Administrative and Cultural District in 2024 is the best overall, and that the skyline of the New Administrative and Cultural District has developed into one of the city images of Hefei, and that its optimisation can be considered in terms of the “quality of the surrounding ecological environment”.

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Section: Of 19mentioning

confidence: 99%

A Study on the Regional Evolution of the Hefei Political and Cultural New District Skyline Based on Exploratory Factor Analysis and Semantic Segmentation

Zhang,

Yuan

et al. 2024

Buildings

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“…The association between green visibility and residents' mental health can be analyzed by using the green visibility indicator in separated streetscape images [79]. Image segmentation can also be used to study walking accessibility (Walkability) and street safety [80,81] Compared to the widespread use of image segmentation models, models based on image aesthetic quality assessment have not yet received a great deal of attention in the landscape discipline at present, and the difference lies in the fact that the two yield different results: image segmentation models are mainly used to quantify landscape elements in streetscape images [82], while image aesthetic quality assessment models are mainly used to quantify the overall aesthetic quality of a streetscape image [83]. In addition, image segmentation models often do not need to be fine-tuned and can be segmented directly using the pre-trained model, while image aesthetic quality assessment models need to be fine-tuned with sample data to match experimental and practical needs [84].…”

Section: Image Segmentation Modelmentioning

confidence: 99%

A New Approach to Landscape Visual Quality Assessment from a Fine-Tuning Perspective

Fan,

Chen,

Yocom

2024

Land

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Various methods for evaluating the visual quality of landscapes have been continuously studied. In the era of the rapid development of big data, methods to obtain evaluation data efficiently and accurately have received attention. However, few studies have been conducted to optimize the evaluation methods for landscape visual quality. Here, we aim to develop an evaluation model that is model fine-tuned using Scenic Beauty Evaluation (SBE) results. In elucidating the methodology, it is imperative to delve into the intricacies of refining the evaluation process. First, fine-tuning the model can be initiated with a scoring test on a small population, serving as an efficient starting point. Second, determining the optimal hyperparameter settings necessitates establishing intervals within a threshold range tailored to the characteristics of the dataset. Third, from the pool of fine-tuned models, selecting the one exhibiting optimal performance is crucial for accurately predicting the visual quality of the landscape within the study population. Lastly, through the interpolation process, discernible differences in landscape aesthetics within the core monitoring area can be visually distinguished, thereby reinforcing the reliability and practicality of the new method. In order to demonstrate the efficiency and practicality of the new method, we chose the core section of the famous Beijing–Hangzhou Grand Canal in Wujiang District, China, as a case study. The results show the following: (1) Fine-tuning the model can start with a scoring test on a small population. (2) The optimal hyperparameter setting intervals of the model need to be set in a threshold range according to different dataset characteristics. (3) The model with optimal performance is selected among the four fine-tuning models for predicting the visual quality of the landscape in the study population. (4) After the interpolation process, the differences in landscape aesthetics within the core monitoring area can be visually distinguished. We believe that the new method is efficient, accurate, and practically applicable for improving landscape visual quality evaluation.

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Understanding of leaning utility poles for visual monitoring of power distribution infrastructure

Wang,

Liu,

Wang

et al. 2024

J Civil Struct Health Monit

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The economic value of urban landscapes in a suburban city of Tokyo, Japan: A semantic segmentation approach using Google Street View images

Cited by 8 publications

References 32 publications

A Study on the Regional Evolution of the Hefei Political and Cultural New District Skyline Based on Exploratory Factor Analysis and Semantic Segmentation

A Study on the Regional Evolution of the Hefei Political and Cultural New District Skyline Based on Exploratory Factor Analysis and Semantic Segmentation

A New Approach to Landscape Visual Quality Assessment from a Fine-Tuning Perspective

Understanding of leaning utility poles for visual monitoring of power distribution infrastructure

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