Deep Learning Model Identifies Incorrect Geographic Coordinates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current networked computer systems face inaccuracies in predicting geographic locations, leading to inefficient route planning and service delivery, as the stored geographic locations for places often do not match the actual locations traveled by users.
Innovation Solution
A deep learning model, such as a convolutional neural network, is trained using route information and satellite imagery to identify incorrect geographic locations by analyzing traveled routes and corresponding geographic areas, triggering verification operations to correct or update the stored locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stored geographic locations are used for service requests, then service delivery can proceed without additional verification, but the accuracy of geographic positioning deteriorates because stored locations often do not match actual user-traveled locations
Solution Approach 1:
The system performs preliminary verification of geographic locations by analyzing historical route data before finalizing location storage. Route information containing actual traveled paths is processed to validate whether stored geographic coordinates accurately represent the intended destination, correcting discrepancies before they affect service delivery
Solution Approach 2:
The system establishes a feedback loop where actual user-traveled routes are continuously collected and used to verify and update stored geographic locations. The analysis module compares historical route data with stored coordinates, and when mismatches are detected, the system automatically corrects the stored locations, creating a self-improving system that increases accuracy over time
2Measurement precision
If route information and satellite imagery analysis are performed to verify geographic locations, then geographic positioning accuracy improves, but system complexity increases due to the need for deep learning models and data processing
Solution Approach 1:
The system performs self-verification of geographic locations by automatically analyzing its own stored route data and satellite imagery. The deep learning model processes historical route information and satellite images to autonomously detect and correct inaccurate geographic coordinates without requiring external manual verification, making the complex analysis process self-contained
Solution Approach 2:
The system replaces manual geographic location verification with automated deep learning analysis. Instead of human operators manually checking coordinates against maps and route data, a convolutional neural network automatically processes satellite imagery and route information to verify and correct geographic locations, substituting mechanical human analysis with automated computational processing
Data Source
AI summary
Systems and methods of identifying incorrect coordinate prediction using route information are disclosed herein. In some example embodiments, a computer system receives route information and geographic image data. The route information corresponds to a servicing of a request associated with a place and indicates a travelled route that has been traveled by a user in traveling from an origin location to the place, and the geographic image data represents a geographic area corresponding to the travelled route. The computer system determines that an initial geographic location stored in a database in association with the place is incorrect based on the route information and the geographic image data using a first deep learning model, and then performs a verification operation based on the determining that the stored initial geographic location of the place is incorrect.