While map layers with different types of information are available, creating such layers currently requires substantial engineering effort and time. This disclosure describes the use of machine learning based clustering techniques for automatic generation of new layers in digital maps. Layers are automatically generated and can be inspected or corrected by human moderators. The layers include visualizations of aggregate geolocated data. The visualization can utilize generative AI for custom visuals. Layer creation is thus automated and faster, increasing the types of uses for digital maps. If the user permits, the automatically generated custom layers can include commerce-related layers or can be augmented by integrating the display of personalized advertisements and recommendations

Johnson Jr, Joseph

Tran, Quinn

English

Technical Disclosure Common

Technical Disclosure Commons Defensive Publications Series November 2023 Automatic Creation of Map Layers Using Machine Learning Quinn Tran Joseph Johnson Jr Follow this and additional works at: https://www.tdcommons.org/dpubs_series Recommended Citation Tran, Quinn and Johnson Jr, Joseph, "Automatic Creation of Map Layers Using Machine Learning", Technical Disclosure Commons, (November 07, 2023) https://www.tdcommons.org/dpubs_series/6396 This work is licensed under a Creative Commons Attribution 4.0 License. This 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. Automatic Creation of Map Layers Using Machine Learning ABSTRACT While map layers with different types of information are available, creating such layers currently requires substantial engineering effort and time. This disclosure describes the use of machine learning based clustering techniques for automatic generation of new layers in digital maps. Layers are automatically generated and can be inspected or corrected by human moderators. The layers include visualizations of aggregate geolocated data. The visualization can utilize generative AI for custom visuals. Layer creation is thus automated and faster, increasing the types of uses for digital maps. If the user permits, the automatically generated custom layers can include commerce-related layers or can be augmented by integrating the display of personalized advertisements and recommendations. KEYWORDS ● Digital map ● Map layer ● Layer generation ● Layer discovery ● Geolocated data ● Geospatial visual rendering ● Personalized advertising ● Generative AI ● Computational geometry ● User-generated content (UGC)  2Tran and Johnson Jr: Automatic Creation of Map Layers Using Machine LearningPublished by Technical Disclosure Commons, 2023BACKGROUND Applications 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. Clustering 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. DESCRIPTION This disclosure describes techniques for automatic generation of new layers in applications that use digital maps. The layers can be generated automatically by using any suitable clustering algorithm to identify topic clusters based on data regarding various user activities obtained with user permission. For example, with user permission, data such as search queries, location, check-ins, reviews, etc., can be clustered to identify potentially useful, interesting, or relevant topics, such as “tourist attractions,” “historical landmarks,” “shopping zones,” “crowded areas,” “noisy locations,” “construction sites,” “scenic routes,” “disease infections,” etc.  The 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 3Defensive Publications Series, Art. 6396 [2023]https://www.tdcommons.org/dpubs_series/6396corresponding underlying geolocated data can be rendered in aggregate on the map using suitable geospatial visualization techniques, such as heatmaps, topographies, icons, etc. For instance, geolocated numerical data, such as crowd density, sound levels, traffic volume, etc., can be rendered as a heatmap with colors varying based on the value of the data in a given map region, such as a landmark, neighborhood, city, state, etc. For example, noisier thoroughfares can be displayed in darker shades compared to quieter streets in the town that are shown in a lighter shade. The data for any automatically generated layer is presented on the map in an aggregate form with no information on individual data values.  Fig. 1: Rendering automatically generated layers on a digital map 4Tran and Johnson Jr: Automatic Creation of Map Layers Using Machine LearningPublished by Technical Disclosure Commons, 2023Fig. 1 shows an example operational implementation of the techniques described in this disclosure. A user is viewing a map of Manhattan, NY in a digital map application (104) on a device (102). The map (112) includes a set of layers that are automatically generated via a suitable clustering algorithm (110) by processing various pieces of information obtained from a variety of online sources (106).  With the user’s permission, the user’s preferences, context, and historical actions (108) can be taken into account to personalize the selection of the layers shown to the user. The topmost relevant layers are made available to the user within the application user interface (UI). In the example shown in Fig. 1, the automatically generated layers include “noise,” “crowd,” “scenic,” and “wifi.” As Fig. 1 shows, viewing the “noise” layer on the map results in the user being presented with heatmaps of noise levels (114) in various parts of the city, with redder shades indicating areas with higher noise and grayer shades marking quieter places. With user permission, the techniques described herein can be employed for automatically generating map layers covering a variety of information, such as crowds, noise, construction activity, scenic areas, tourist attractions, nightlife, community sentiment, open wifi hotspots, safety, etc. For instance, user-generated content, such as reviews, social media posts, comments, etc., related to locations within a given map area can be clustered based on the underlying aggregated sentiment related to those locations to provide an overall sentiment score for the area. The relevant data to be clustered for the automatic generation of map layers can be obtained with permission from one or more information sources, such as social media, online review and ratings platforms, online search engines, activity analytics, sensors on user devices, satellites, neighborhood-mapping vehicles, construction alerts, etc. As appropriate, layers can be generated by correlating multiple relevant pieces of information. For example, scenic areas can 5Defensive Publications Series, Art. 6396 [2023]https://www.tdcommons.org/dpubs_series/6396be determined based on a combination of image processing (quality and content of geolocated photos or video frames), natural language processing (sentiment and specific phrases such as “amazing view,” “scenic stop,” etc.), and appropriate heuristics (e.g., “bodies of water and mountains tend to have stunning vistas”). Computational geometry techniques can be used to compute a skyline to determine what is visible from a particular viewpoint (e.g., from a vehicle).  The digital map application can then provide the automatically generated layers to users, who can use the information from the layers for a wide variety of tasks. For instance, users can invoke the “noise” layer when searching for homes in quiet neighborhoods or view the “construction” layer to avoid travel delays due to construction sites. A layer can potentially be used in opposite ways depending on the context and the task. For example, a user can use the “crowd” layer to identify less busy walking routes or to identify crowded areas to visit because these are likely to be popular hotspots of interest at a given time (e.g., a live music concert). Automatically generated custom layers can be augmented with user permission by integrating the display of personalized advertisements and recommendations relevant to the layer. For example, a layer for “high-end fashion” can be rendered with prominent pins advertising stores of luxury brands. In some cases, the topic of the layer itself can be of a commercial nature, e.g., a “Coffee” layer that advertises coffee shops on the map. The automatically generated layers are limited to specific domains that are of user interest and that meet safety and legal requirements. The automatically generated layers can be verified by a human moderator prior to being made available to users. Automatically generated layers that are shown and used frequently can be featured or permanently displayed. If geospatial visual rendering or stylization of a layer requires custom visual content (e.g., 3D objects), such content can be created via suitable generative artificial 6Tran and Johnson Jr: Automatic Creation of Map Layers Using Machine LearningPublished by Technical Disclosure Commons, 2023intelligence (AI) techniques. Generative AI functionality can be implemented to run locally on the user device to avoid network latency and limit the used bandwidth.  The techniques described herein can be implemented within any digital mapping or navigation application, service, or platform, including those integrated within automobiles. Alternatively, or in addition, the techniques can be made available via a standard mechanism, such as an application programming interface (API). Implementation of the techniques described in the disclosure can enable applications that involve digital maps to create and provide a variety of map layers with greater speed and accuracy compared to the current manual operation. Moreover, the layers and corresponding recommendations can be personalized to user interests with permission. The techniques can enable digital mapping applications to support a variety of user tasks in an efficient, effective, and versatile manner, thus enhancing their user experience (UX). Further to the descriptions above, a user may be provided with controls allowing the user to make an election as to both if and when systems, programs or features described herein may enable collection of user information (e.g., information about a user’s social network, social actions or activities, profession, a user’s preferences, or a user’s current location), and if the user is sent content or communications from a server. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user’s identity may be treated so that no personally identifiable information can be determined for the user, or a user’s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over what information is collected about the user, how that information is used, and what information is provided to the user. 7Defensive Publications Series, Art. 6396 [2023]https://www.tdcommons.org/dpubs_series/6396CONCLUSION While map layers with different types of information are available, creating such layers currently requires substantial engineering effort and time. This disclosure describes the use of machine learning based clustering techniques for automatic generation of new layers in digital maps. Layers are automatically generated and can be inspected or corrected by human moderators. The layers include visualizations of aggregate geolocated data. The visualization can utilize generative AI for custom visuals. Layer creation is thus automated and faster, increasing the types of uses for digital maps. If the user permits, the automatically generated custom layers can include commerce-related layers or can be augmented by integrating the display of personalized advertisements and recommendations. 8Tran and Johnson Jr: Automatic Creation of Map Layers Using Machine LearningPublished by Technical Disclosure Commons, 2023

Automatic Creation of Map Layers Using Machine Learning

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Automatic Creation of Map Layers Using Machine Learning

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