[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124176-en":3,"doc-seo-124176-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124176,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A Framework for Automatic Selection of Indoor Landmarks using Machine Learning Algorithms and Shapley Additive Explanations - Abstract","Landmarks are prominent objects that support natural indoor navigation, yet route guidance requires selecting the most salient candidates. Weighted linear models treat landmark salience as absolute values, which conflicts with the definition of salience relative to surrounding context. This work proposes a probability-based soft classification framework that aggregates visual, structural, and semantic salience measures, trains and compares machine learning classifiers, interprets local feature contributions via Shapley Additive Explanations, and applies probability calibration for fine-grained landmark suitability. Preliminary results indicate strong performance with boosting-based methods, with functional uniqueness, category, and intensity measures playing key roles; further validation with user studies remains necessary.","A Framework for Automatic Selection of Indoor Landmarks using Machine Learning Algorithmsand Shapley Additive Explanations  \nAtakan Bilgili*, Alper Sen*  \nDepartment of Geomatic Engineering, Faculty of Civil Engineering, Yıldız Technical University, Esenler, Istanbul 34220, Türkiye [atakanb@yildiz.edu.tr](atakanb@yildiz.edu.tr), [alpersen@yildiz.edu.tr](alpersen@yildiz.edu.tr)  \nAbstract. Landmarks are salient objects in an environment compared to their surroundings. However, a challenge of landmark-based navigation is selecting the most salient landmarks to include in route instructions. Current approaches mainly adopt weighted linear models, which assume that landmarks have absolute salience values. However, this contradicts the definition of landmarks as being salient in comparison to their surroundings. In this work-in-progress study, a probability-based soft classification approach is proposed to automatically select indoor landmarks. Specifically, we aggregated fundamental salience measures regarding visual, structural, and semantic dimensions from related studies to create an indoor landmark dataset. Then we, compared the performances of machine learning classifiers with several metrics and interpreted the local contributions of salience measures. Finally, we utilized a probability calibration technique that allows for finer-grained representations of indoor landmarks to include them in the route guidance process. According to the preliminary results of this study, boosting-based machine learning algorithms provide remarkable results, and functional uniqueness, category, and intensity measures are considered more important to select indoor landmarks. Moreover, our soft probability-based classification framework seems promising for selecting and representing landmarks in a fine-grained manner. However, the feasibility of the proposed framework should be further validated with user studies.  \nKeywords. Indoor navigation, landmark, machine learning, Shapley Additive Explanations  \nPublished in “Proceedings of the 18th International Conference on Location Based Services (LBS 2023)”, edited by Haosheng Huang,  \nNico Van de Weghe and Georg Gartner, LBS 2023, 20-22 November 2023 Ghent, Belgium.  \nThis contribution underwent single-blind peer review based on the paper. [https://doi](https://doi.org/10.34726/5702 |)[.](https://doi.org/10.34726/5702 |)[org/10](https://doi.org/10.34726/5702 |)[.](https://doi.org/10.34726/5702 |)[34726/5702 |](https://doi.org/10.34726/5702 |) © Authors 2023. CC BY 4.0 License.  \n1. Introduction  \nLandmarks are defined as prominent objects in an environment that are easily recognizable by their visual, structural, or semantic attributes compared to their surroundings (Sorrows and Hirtle 1999) . Hence, including landmarks in the route instructions provides a more natural wayfinding experience, reducing the cognitive load of pedestrians and facilitating wayfinding by helping to organize their spatial mental representations (Hu et al. 2020; Zhou et al. 2022) .  \nA challenge of landmark-based route communication is selecting the most salient objects along a particular route. Specifically, an object should be more salient than its surroundings regarding visual (e.g., color, size, shape), structural (e.g., location, centrality, visibility), or semantic/cognitive (e.g., function, uniqueness, socio-cultural) dimensions (Sorrows and Hirtle 1999) to be considered as a landmark candidate. Current approaches for selecting landmarks mainly emphasize weighted linear models. Typically, studies have adopted a linear model and a set of salience measures that consider visual, structural, and semantic dimensions to compute the salience value of a landmark candidate, following the work of Raubal and Winter (2002) . The problem with the weighted linear model-based approaches is it assumes that landmarks have absolute salience (Zhou et al. 2022), which contradicts the definition of landmarks as being salient with respect","cbCaitSDFgszRFgH","https://ap.wps.com/l/cbCaitSDFgszRFgH","pdf",660098,1,6,"English","en",105,"# Introduction\n## Problem statement: landmark salience for route communication\n## Proposed approach and contributions\n# Methodology\n## Overview","[{\"question\":\"How does the study interpret which salience measures matter most?\",\"answer\":\"It uses SHAP (Shapley Additive Explanations) to analyze local contributions of visual, structural, and semantic salience measures to the final predictions.\"}]","A Framework for Automatic Selection of Indoor Landmarks using Machine Learning Algorithms and Shapley Additive Explanations - Abstract | PDF",1785820861,15,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"a-framework-for-automatic-selection-of-indoor-landmarks-using-machine-learning-algorithms-and-shapley-additive-explanations-abstract","",{"@graph":36,"@context":77},[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/a-framework-for-automatic-selection-of-indoor-landmarks-using-machine-learning-algorithms-and-shapley-additive-explanations-abstract/124176/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study interpret which salience measures matter most?","Question",{"text":75,"@type":76},"It uses SHAP (Shapley Additive Explanations) to analyze local contributions of visual, structural, and semantic salience measures to the final predictions.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]