[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-126567-105":59,"doc-detail-126567-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","an-interpretable-machine-learning-framework-for-measuring-urban-perceptions-from-panoramic-street-view-images-research-framework-and-interpretability","An interpretable machine learning framework for measuring urban perceptions from panoramic street view images - 研究框架与可解释性应用","","Urban perception analysis is increasingly enabled by street view images (SVIs) and deep learning, yet many existing pipelines remain difficult to interpret because they function as end-to-end black boxes, limiting planning value. This work presents a five-step, feature- and result-interpretable machine learning framework using panoptic segmentation, crowdsourced SVI pairwise comparisons quantified with the Elo rating system, and feature-importance with accumulated local effects. Using MIT Place Pulse, it extracts six perception dimensions—wealth, boredom, depression, beauty, safety, liveliness—and demonstrates deployment in Inner London at the output-area level with validation against real-world crime rates.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-interpretable-machine-learning-framework-for-measuring-urban-perceptions-from-panoramic-street-view-images-research-framework-and-interpretability/126567/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-interpretable-machine-learning-framework-for-measuring-urban-perceptions-from-panoramic-street-view-images-research-framework-and-interpretability/126567.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-18","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why do existing urban perception frameworks have limited planning usefulness?","Question",{"text":112,"@type":113},"Many rely on end-to-end deep learning structures that behave like black boxes, reducing interpretability and limiting their value as planning support tools.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the proposed framework designed to achieve?",{"text":117,"@type":113},"A five-step machine learning framework that extracts neighborhood-level urban perceptions from panoramic SVIs while emphasizing feature and result interpretability.",{"name":119,"@type":110,"acceptedAnswer":120},"How are perception labels obtained and quantified in the framework?",{"text":121,"@type":113},"Crowdsourced SVI pairwise comparisons are quantified using the Elo rating system, producing measurable perception dimensions.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},126567,1785933371,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":26},687207017582,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","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","cbCaio2nwAflQeA7","https://ap.wps.com/l/cbCaio2nwAflQeA7","pdf",9261945,24,"English","# Summary\n# Introduction\n## Urban perception and its importance\n## Limitations of traditional data collection\n## Promise of street view images and deep learning","[{\"question\":\"Why do existing urban perception frameworks have limited planning usefulness?\",\"answer\":\"Many rely on end-to-end deep learning structures that behave like black boxes, reducing interpretability and limiting their value as planning support tools.\"},{\"question\":\"What is the proposed framework designed to achieve?\",\"answer\":\"A five-step machine learning framework that extracts neighborhood-level urban perceptions from panoramic SVIs while emphasizing feature and result interpretability.\"},{\"question\":\"How are perception labels obtained and quantified in the framework?\",\"answer\":\"Crowdsourced SVI pairwise comparisons are quantified using the Elo rating system, producing measurable perception dimensions.\"}]","An interpretable machine learning framework for measuring urban perceptions from panoramic street view images - 研究框架与可解释性应用 | PDF"]