User-Corrected Image Geocoding with Validation Scoring
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Solution Overview
Problem
The existing systems for collecting and reconstructing real-world images using mobile devices often suffer from noise discrepancies between camera and GPS or laser sensors, leading to inaccurate geographic coordinates, especially in urban areas with reduced GPS signal reception.
Innovation Solution
A system and method that allows users to correct image coordinates by submitting more accurate geographic coordinates determined by their device, which are validated through a scoring function to adjust the original coordinates stored in the image database, ensuring improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world images are collected using a camera and GPS device on a moving vehicle, then geographic coordinates can be obtained for mapping, but noise and discrepancies between the camera and GPS device cause coordinate inaccuracies
Solution Approach 1:
The system implements feedback by allowing users to report actual observed locations of images and comparing these observations with the originally recorded GPS coordinates. The system processes this feedback information to identify and correct coordinate discrepancies, continuously improving accuracy based on user inputs and validation results.
Solution Approach 2:
The system enables self-service through automated validation processes that assess user-submitted coordinate corrections using scoring functions. The validation system automatically determines whether corrections meet accuracy thresholds without requiring manual expert verification for each case, allowing the system to self-correct coordinate errors.
2Reliability
If GPS signal reception is reduced in urban areas, then coordinate data may be incomplete or inaccurate, but the system still needs to provide location information
Solution Approach 1:
The system introduces an intermediary validation process that acts as a mediator between user-submitted location data and the final coordinate database. This validation system assesses the reliability of corrections using scoring functions and thresholds, filtering and validating data quality before integration, thereby ensuring reliability even when direct GPS signals are weak.
Solution Approach 2:
The system changes parameters by implementing a scoring mechanism that evaluates multiple factors (user reputation, number of corrections, validation metrics) to determine the reliability of coordinate corrections. This multi-parameter assessment approach allows the system to maintain reliability in urban areas by weighing various indicators of data quality rather than relying solely on raw GPS signal strength.
Data Source
AI summary
A system, computer-implemented method and computer-readable medium for correcting existing coordinates of an image. The image is provided to the client device, the image associated with a first geographic coordinate. A second geographic coordinate is received from the client device representing a location of the client device and an indication that the image resembles surroundings of the client device at the second geographic coordinate, where the second geographic coordinate is different from the first second geographic coordinate. A determination is made as to whether the received second geographic coordinate more accurately represents a location of a camera that took the image than the first geographic coordinate. When the received second coordinate is determined to be more accurate than the first coordinate, updating the first geographic coordinate associated with the image according to the received second geographic coordinate.