Geospatial Image Processing System for Computer Vision Model Validation
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Solution Overview
Problem
State-of-the-art computer vision technologies for identifying objects in geospatial imagery suffer from inaccuracies, including false positives, false negatives, misidentification of boundaries, and attributes, leading to errors and uncertainty.
Innovation Solution
A geospatial image processing system with a user interface that facilitates user tagging and validation of computer vision model detections, allowing for the update of metadata and continuous training of the model to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If state-of-the-art computer vision technologies are used for identifying objects in geospatial imagery, then object detection capability is provided, but accuracy and reliability deteriorate due to false positives, false negatives, boundary misidentification, and attribute misidentification
Solution Approach 1:
The patent implements a feedback mechanism where users can validate or correct computer vision model detections by tagging objects in geospatial imagery. This user feedback is then used to retrain the model, creating a continuous improvement loop that resolves the contradiction between providing automated detection and maintaining high accuracy.
Solution Approach 2:
The system enables self-service through automated computer vision detection that provides initial object identification, which then serves as a basis for user validation. This combination of automated service and human oversight addresses the reliability-precision contradiction by leveraging both machine efficiency and human accuracy.
2Reliability
If user tagging and validation processes are implemented to improve model accuracy, then detection accuracy improves, but system complexity and time consumption increase
Solution Approach 1:
The system performs preliminary automated detection before user validation, providing users with pre-processed results that require only confirmation or correction rather than complete analysis from scratch. This reduces the complexity of the user interaction while maintaining accuracy improvements.
Solution Approach 2:
Users are presented with a subset of detections for validation rather than requiring complete manual tagging of all objects. The system processes only the necessary portion of data through user validation, balancing accuracy improvement with reduced operational complexity.
3Manufacturing precision
If comprehensive user validation of all detections is performed, then model training accuracy improves, but productivity and processing speed decrease
Solution Approach 1:
The system validates only a partial set of detections that are most beneficial for model improvement, rather than requiring complete validation of all detections. This selective approach maintains training precision while improving overall productivity.
Solution Approach 2:
The system implements continuous model retraining using accumulated user feedback over time, rather than requiring complete validation batches. This allows the model to improve progressively while maintaining high productivity through incremental updates.
Data Source
AI summary
An exemplary geospatial image processing system generates, based on multiple detections of an object of interest detected by a computer vision model in multiple, correlated images of a geospatial location captured from different camera viewpoints, user interface content that includes a visual indication of the detected object of interest superimposed at an object position on a view of the geospatial location. The system provides the user interface content for display in a graphical user interface view of a user interface and provides, by way of the user interface, a user interface tool configured to facilitate user validation of one or more of the multiple detections of the object of interest. The system may receive, a user validation of one or more of the multiple detections of the object of interest and may train the computer vision model based on the user validation. Corresponding methods and systems are also disclosed.