[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126761-en":3,"doc-seo-126761-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126761,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","An interpretable machine learning framework for measuring urban perceptions from panoramic street view images - Research insights","An interpretable machine learning framework extracts neighborhood-level urban perceptions from panoramic street view images by transforming visual scenes into six perception dimensions. The approach emphasizes interpretability through panoptic segmentation to capture human-recognizable visual elements, Elo-based quantification of crowdsourced image pairwise comparisons, and model explanations using feature importance and accumulated local effects. Using the MIT Place Pulse data, the framework operationalizes wealth, boredom, depression, beauty, safety, and liveliness, and demonstrates practical deployment in Inner London with validation against real-world crime rates.","iScience  \nll  \nOPEN ACCESS  \nArticle  \nAn interpretable machine learning framework for measuring urban perceptions from panoramic street view images  \nYunzhe Liu, Meixu Chen, Meihui Wang, Jing Huang, Fisher Thomas, Kazem Rahimi, Mohammad Mamouei  \n[yunzhe.liu@ic.ac.uk](yunzhe.liu@ic.ac.uk) (Y. L.) [maychen@liverpool.ac.uk](maychen@liverpool.ac.uk)[ ](maychen@liverpool.ac.uk)(M.C.)  \nHighlights  \nAn interpretable framework to extract urban perceptions from panoramic SVIs  \nUsing panoptic segmentation to identify human-recognizable visual elements  \nCrowdsourced SVI pairwise comparisons are quantified via the Elo rating system  \nUsing feature importance and accumulated local effects to improve interpretability  \nLiu et al. , iScience 26, 106132 March 17, 2023 ª 2023 The Authors.  \n[https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)[j.isci.2023.106132](j.isci.2023.106132)  \niScience  \nll  \nOPEN ACCESS  \nArticle  \nAn interpretable machine learning framework for measuring urban perceptions  \nfrom panoramic street view images  \nYunzhe Liu,1,5,6,* Meixu Chen,2,* Meihui Wang,3 Jing Huang, 1,4 Fisher Thomas,1 Kazem Rahimi, 1 and Mohammad Mamouei1  \nSUMMARY  \nThe proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their endto-end structure and ‘‘black-box’’ nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate.  \nINTRODUCTION  \nAs the environment where most human activities occur, cities can be characterized as an interchange hub for capital, logistics, labor, and information, shaping and influencing the lives of their residents from multiple perspectives.1 ,2 Numerous studies have shown that the physical appearance of cities plays a pivotal role in residents’ psychological feelings toward the urban built environment, consequently influencing their behaviors.3–10 Such human-perceived experience of the urban environment is also known as urban perception,11 , 12 together with urban identity, formulating important concepts in urbanism and urban design.13–15 Given the spatial heterogeneity and complexity of the urban built environment in terms of overall environmental quality and physical appearance, urban perceptions vary across different city areas. Therefore, research on urban perception offers a promising perspective that assists urban analysts in gaining insights into urban morphology and metabolism and the way residents perceive their living neighborhood areas, facilitating evidence-based policymaking in urban planning and regeneration.  \nGathering information about visual surroundings from the urban built environment and evaluating their influences on human perceptions have a long research history.7 , 16–20 However, most previous studies relied on traditional data collection approaches, such as field surveys, questionnaires, and interviews, which are costly, error-prone, and time-consuming. As such, these studies encountered challenges in knowledge discovery and generalization, particularly for large-scale urban regions, due to the lack of the fine-granularity and high throughput of the investigation methods.2","cbCairiQW48PIRhF","https://ap.wps.com/l/cbCairiQW48PIRhF","pdf",9261878,1,24,"English","en",105,"# Summary\n# Introduction\n# Framework Overview\n## Panoptic segmentation for visual elements\n## Crowdsourced pairwise comparisons via Elo rating\n## Interpretability with feature importance and local effects\n# Practical Deployment and Validation\n## Inner London visualization at OA level\n## Comparison with real-world crime rates","[{\"question\":\"What problem does the proposed framework address?\",\"answer\":\"Existing SVI-based analysis often behaves like a black box, limiting planning value. The framework aims to measure urban perceptions while remaining interpretable.\"},{\"question\":\"How are human-recognizable visual elements extracted?\",\"answer\":\"The framework uses panoptic segmentation to identify visual elements that are recognizable to humans within panoramic street view images.\"},{\"question\":\"How are crowdsourced perceptions quantified and interpreted?\",\"answer\":\"Crowdsourced pairwise comparisons are converted into quantitative scores using the Elo rating system. Interpretability is further improved with feature importance and accumulated local effects.\"}]","An interpretable machine learning framework for measuring urban perceptions from panoramic street view images - Research insights | PDF",1785934647,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-interpretable-machine-learning-framework-for-measuring-urban-perceptions-from-panoramic-street-view-images-research-insights","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-interpretable-machine-learning-framework-for-measuring-urban-perceptions-from-panoramic-street-view-images-research-insights/126761/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the proposed framework address?","Question",{"text":75,"@type":76},"Existing SVI-based analysis often behaves like a black box, limiting planning value. The framework aims to measure urban perceptions while remaining interpretable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are human-recognizable visual elements extracted?",{"text":80,"@type":76},"The framework uses panoptic segmentation to identify visual elements that are recognizable to humans within panoramic street view images.",{"name":82,"@type":73,"acceptedAnswer":83},"How are crowdsourced perceptions quantified and interpreted?",{"text":84,"@type":76},"Crowdsourced pairwise comparisons are converted into quantitative scores using the Elo rating system. 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