[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120876-en":3,"doc-seo-120876-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120876,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Automatic Creation of Map Layers Using Machine Learning","Digital map applications offer many layer types, but adding new layers often depends on time-consuming engineering work. This disclosure presents machine-learning clustering methods that automatically generate new map layers from geolocated data gathered with user permission. Generated layers can be reviewed and corrected by human moderators, and visualizations may include generative-AI custom visuals. With user approval, custom layers can incorporate commerce-oriented content and personalized advertisement and recommendation displays.","Technical Disclosure Commons  \nDefensive Publications Series  \nNovember 2023  \nAutomatic Creation of Map Layers Using Machine Learning Quinn Tran  \nJoseph Johnson Jr  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nTran, Quinn and Johnson Jr, Joseph, \"Automatic Creation of Map Layers Using Machine Learning\", Technical Disclosure Commons,(November 07, 2023)  \n[https://www.tdcommons.org/dpubs_series/6396](https://www.tdcommons.org/dpubs_series/6396)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nAutomatic Creation of Map Layers Using Machine Learning  \nABSTRACT  \nWhile map layers with different types of information are available, creating such layers  \ncurrently requires substantial engineering effort and time. This disclosure describes the use of  \nmachine learning based clustering techniques for automatic generation of new layers in digital  \nmaps. Layers are automatically generated and can be inspected or corrected by human  \nmoderators. The layers include visualizations of aggregate geolocated data. The visualization can  \nutilize generative AI for custom visuals. Layer creation is thus automated and faster, increasing  \nthe types of uses for digital maps. If the user permits, the automatically generated custom layers  \ncan include commerce-related layers or can be augmented by integrating the display of  \npersonalized advertisements and recommendations.  \nKEYWORDS  \n● Digital map  \n● Map layer  \n● Layer generation  \n● Layer discovery  \n● Geolocated data  \n● Geospatial visual rendering  \n● Personalized advertising  \n● Generative AI  \n● Computational geometry  \n● User-generated content (UGC)  \nPublished by Technical Disclosure Commons, 2023 2  \nBACKGROUND  \nApplications that use digital maps include the functionality to display the map using various relevant layers, such as satellite view, bicycle paths, etc. Currently, the availability and inclusion of any additional layer in a digital map is dependent on manual processes that require effort from engineering teams. The slow pace of manual selection and implementation of layers can result in layers not being available when users are likely to find them the most relevant and useful. For example, delays in implementing new layers can result in a map layer about infections during a pandemic being available only at the tail end of the pandemic when interest in the data has waned.  \nClustering is a machine learning technique that can be used to group data points together based on similarity. Some of the most common clustering algorithms include k-means clustering, hierarchical clustering (such as agglomerative clustering), and density-based clustering.  \nDESCRIPTION  \nThis disclosure describes techniques for automatic generation of new layers in  \napplications that use digital maps. The layers can be generated automatically by using any  \nsuitable clustering algorithm to identify topic clusters based on data regarding various user  \nactivities obtained with user permission. For example, with user permission, data such as search  \nqueries, location, check-ins, reviews, etc., can be clustered to identify potentially useful,  \ninteresting, or relevant topics, such as “tourist attractions,”“historical landmarks,”“shopping  \nzones,”“crowded areas,”“noisy locations,”“construction sites,”“scenic routes,”“disease  \ninfections,” etc.  \nThe most relevant, popular, or important of these topics can be made available to users as layers on a digital map. When users choose to view any of the generated layers, the  \n[https://www.tdcommons.org/dpubs_series/6396](https://www.tdcommons.org/dpubs_series/6396) 3  \ncorresponding underlying geolocated data can b","cbCaicQGSeFvkfeW","https://ap.wps.com/l/cbCaicQGSeFvkfeW","pdf",533306,1,"English","en",105,"# Background\n## Digital map layers and manual implementation\n## Clustering as a machine learning technique\n# Description\n## Automatic layer generation via topic clustering\n## Rendering aggregated geolocated data\n## Example system implementation and personalization","[{\"question\":\"How does the disclosure generate new map layers automatically?\",\"answer\":\"It uses machine-learning clustering algorithms to identify topic clusters from geolocated data related to user activities, collected with user permission.\"},{\"question\":\"What kinds of map visualizations can be produced from the generated layers?\",\"answer\":\"The disclosure describes rendering aggregated underlying geolocated data using techniques such as heatmaps, topographies, and icons.\"},{\"question\":\"Can the automatically created layers be reviewed or improved?\",\"answer\":\"Yes. The layers are automatically generated and can be inspected or corrected by human moderators.\"}]","Automatic Creation of Map Layers Using Machine Learning | PDF",1785732447,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"automatic-creation-of-map-layers-using-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/automatic-creation-of-map-layers-using-machine-learning/120876/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the disclosure generate new map layers automatically?","Question",{"text":74,"@type":75},"It uses machine-learning clustering algorithms to identify topic clusters from geolocated data related to user activities, collected with user permission.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What kinds of map visualizations can be produced from the generated layers?",{"text":79,"@type":75},"The disclosure describes rendering aggregated underlying geolocated data using techniques such as heatmaps, topographies, and icons.",{"name":81,"@type":72,"acceptedAnswer":82},"Can the automatically created layers be reviewed or improved?",{"text":83,"@type":75},"Yes. 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