Geo-Semantic Index for Map Search via Image Analysis
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Solution Overview
Problem
Current search systems for locating facilities and physical attributes are cumbersome and often yield inconsistent or incorrect results, as they rely on manual labeling and fixed records that may be incomplete or outdated, failing to effectively accommodate user activity needs.
Innovation Solution
A computer-implemented method using image content analysis to generate a geo-semantic index of locations, which identifies feature and attribute types associated with user activities, enabling more comprehensive search recommendations by analyzing street-level imagery and updating the index periodically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling and fixed records are used to identify locations with facilities and physical attributes, then the search system can provide location information, but the results are inconsistent and incorrect due to incomplete or outdated data
Solution Approach 1:
The patent replaces manual labeling (mechanical human operation) with automated image content analysis using AI/ML models. The system processes street-level imagery to automatically detect and classify facilities and physical attributes, substituting human labor with computational algorithms that provide consistent, scalable, and up-to-date location information without manual intervention
Solution Approach 2:
The system enables self-updating of location data by continuously analyzing new street-level imagery. The image analysis pipeline automatically detects changes in facilities and attributes without requiring manual data entry or updates, allowing the search system to maintain current information through autonomous processing of incoming image data
2Measurement precision
If typical search queries are used to find nearby locations with facilities, then users can perform searches, but the results fail to accurately identify locations that can accommodate specific user activities
Solution Approach 1:
The patent transforms the search approach by changing from keyword-based queries to image-feature-based analysis. The system extracts visual features from street-level imagery (such as presence of playground equipment, seating areas, shade structures) and uses these visual parameters to determine activity suitability, providing more precise matching between user needs and location characteristics while maintaining simple search interfaces
3Duration of action of stationary object
If facilities and physical attributes are manually mapped, then location data can be stored, but the data becomes outdated quickly due to frequent changes in facilities
Solution Approach 1:
The system implements continuous automated image analysis of street-level imagery to maintain up-to-date location data. Rather than periodic manual updates, the pipeline continuously processes new imagery to detect changes in facilities and attributes, ensuring the database reflects current conditions without interruption or manual intervention
Solution Approach 2:
The system performs preliminary analysis of street-level imagery to pre-identify facilities and physical attributes before they are needed for search queries. By proactively processing images and extracting feature information in advance, the system maintains ready-to-use, current location data that can quickly respond to user searches without delay
Data Source
AI summary
The present disclosure provides systems and methods that enable map search recommendations based on a geo-semantic index developed using image content analysis. In one example, a computer-implemented method can include obtaining, by one or more computing devices, a vocabulary of image feature types associated with user activities. The method can include obtaining a collection of imagery. The method can include performing image content analysis on the collection of imagery based on the vocabulary of image feature types. The method can include generating at least one activity score for each of a plurality of location cells in a geo-semantic index based at least in part on the vocabulary of image feature types. The method can include populating the geo-semantic index of location cells, the geo-semantic index of location cells including data indicative of the at least one activity score for each location cell. The method can include providing the geo-semantic index of location cells for use in generating location recommendations in response to a query.