Georeferenced Data Clustering for Digital Map Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital maps lack fine-grained local georeferenced data, which prevents the display of smaller objects of interest, due to insufficient information about their locations or reliability.
Innovation Solution
A method and system that utilize image metadata from user devices to identify and cluster images associated with specific objects at given locations, determining the relevance and trustworthiness of the images through clustering and proximity techniques, and update digital maps with cartographical features based on this information, including images and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional digital maps use traditional mapping data sources, then map coverage is maintained, but fine-grained local georeferenced data and smaller objects of interest are missing
Solution Approach 1:
The patent combines multiple image metadata sources (GPS coordinates, timestamps, device identifiers) with image analysis results to create clustered georeferenced data groups. This merging of diverse data types enables the discovery of fine-grained local features while maintaining reliability through cross-validation of multiple data sources.
Solution Approach 2:
The system uses user-generated images as copies of real-world objects to build digital map features. By analyzing photographs taken by users and extracting georeferenced information, the system creates digital representations of smaller objects of interest without requiring traditional surveying methods.
2Measurement precision
If digital maps include more detailed local features, then map accuracy and user experience improve, but data processing complexity increases
Solution Approach 1:
The patent segments the large dataset of user images into smaller clusters based on spatial proximity and temporal proximity. By dividing the processing task into manageable clusters rather than analyzing all images globally, the system achieves high location accuracy while keeping computational complexity tractable through localized processing.
Solution Approach 2:
The system processes images in clusters rather than requiring complete processing of all possible data. By using threshold-based clustering (processing enough images to confidently identify a feature), the system achieves sufficient accuracy without the excessive complexity of exhaustive analysis.
3Quantity of substance
If user-generated images are used to populate digital maps, then fine-grained local data is captured, but data reliability and trustworthiness become concerns
Solution Approach 1:
The system implements feedback through clustering validation, where images are grouped based on consistent metadata patterns. Only clusters that meet certain density and consistency thresholds are converted to map features, providing automatic quality feedback that filters unreliable user-generated content while preserving valuable local data.
Solution Approach 2:
The patent performs preliminary clustering and validation of user images before adding them to the digital map. By pre-processing and validating data in batches, the system ensures reliability criteria are met before commitment to the permanent map database, reducing the risk of incorporating unreliable content.
Data Source
AI summary
Methods, systems and computer readable media for identifying local georeferenced data are described. A method can include receiving a plurality of images and corresponding metadata for each image, the metadata including location information indicating where the corresponding image was acquired and object information indicating one or more objects shown in the corresponding image. The method can also include determining based on the images and corresponding metadata, that a group of images within the plurality of images is associated with a given object at a given location. The method can further include updating a digital map to include a cartographical feature based on the determination of the given object at the given location, wherein the cartographical feature is caused to be displayed on the digital map at a location corresponding to the given location.