{"title":"Evaluating the subjective perceptions of streetscapes using street-view images","authors":"Yoshiki Ogawa , Takuya Oki , Chenbo Zhao , Yoshihide Sekimoto , Chihiro Shimizu","doi":"10.1016/j.landurbplan.2024.105073","DOIUrl":null,"url":null,"abstract":"<div><p>Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-scale urban regions at high spatial resolutions has been difficult. We present a deep-learning-based multilabel classification model that can measure 22 subjective perceptions scores from street-view images. This model uses the results of a web questionnaire survey encompassing 22 subjective perceptions, with 8.8 million responses. Our model demonstrates high accuracy (0.80–0.91) in measuring subjective perception scores from street-view images and achieves low computational cost by training on 22 subjective perception relationships. The 22 subjective perceptions were analyzed using PCA and k-means analysis. By categorizing the 22 subjective perceptions into a two-dimensional space visualized and grouped into distinct groups—positive, negative, calm, and lively—we unearthed vital insights into the intricate nuances of human perception. In addition, the study used semantic segmentation to extract landscape elements from street-view images and applied ℓ1-regularized sparse modeling to identify the landscape elements structurally correlating with each subjective perception class. The analysis revealed that only seven out of nineteen landscape elements significantly correlated with subjective impressions, and these effects varied by class. Notably, sky coverage positively influences positive subjective perceptions, such as attractiveness and calmness, but negatively affects lively impressions. The proposed model can be used to map the overall image of a city and identify landscape design issues in community development design.</p></div>","PeriodicalId":54744,"journal":{"name":"Landscape and Urban Planning","volume":null,"pages":null},"PeriodicalIF":7.9000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169204624000720/pdfft?md5=f7e3eaec1ac0c4ee30951b331719e246&pid=1-s2.0-S0169204624000720-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Landscape and Urban Planning","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169204624000720","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-scale urban regions at high spatial resolutions has been difficult. We present a deep-learning-based multilabel classification model that can measure 22 subjective perceptions scores from street-view images. This model uses the results of a web questionnaire survey encompassing 22 subjective perceptions, with 8.8 million responses. Our model demonstrates high accuracy (0.80–0.91) in measuring subjective perception scores from street-view images and achieves low computational cost by training on 22 subjective perception relationships. The 22 subjective perceptions were analyzed using PCA and k-means analysis. By categorizing the 22 subjective perceptions into a two-dimensional space visualized and grouped into distinct groups—positive, negative, calm, and lively—we unearthed vital insights into the intricate nuances of human perception. In addition, the study used semantic segmentation to extract landscape elements from street-view images and applied ℓ1-regularized sparse modeling to identify the landscape elements structurally correlating with each subjective perception class. The analysis revealed that only seven out of nineteen landscape elements significantly correlated with subjective impressions, and these effects varied by class. Notably, sky coverage positively influences positive subjective perceptions, such as attractiveness and calmness, but negatively affects lively impressions. The proposed model can be used to map the overall image of a city and identify landscape design issues in community development design.
期刊介绍:
Landscape and Urban Planning is an international journal that aims to enhance our understanding of landscapes and promote sustainable solutions for landscape change. The journal focuses on landscapes as complex social-ecological systems that encompass various spatial and temporal dimensions. These landscapes possess aesthetic, natural, and cultural qualities that are valued by individuals in different ways, leading to actions that alter the landscape. With increasing urbanization and the need for ecological and cultural sensitivity at various scales, a multidisciplinary approach is necessary to comprehend and align social and ecological values for landscape sustainability. The journal believes that combining landscape science with planning and design can yield positive outcomes for both people and nature.