Infrastructure Model Labeling Tool with ML Prediction
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Solution Overview
Problem
Ensuring consistent and accurate class labels for infrastructure models is challenging due to differing labeling standards and limited software tools for generating labeled datasets and evaluating machine learning model predictions.
Innovation Solution
A labeling tool that uses a machine learning model to predict element classes, provides a cycle review mode for user confirmation, and offers multiple visualization schemes to compare label and prediction files, facilitating the creation and refinement of accurate class labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and correction of class labels is performed, then labeling accuracy is improved, but time consumption and labor effort increase
Solution Approach 1:
The system pre-processes labeling tasks by automatically generating initial class labels for infrastructure model elements using machine learning models, and pre-organizes review workflows that prioritize elements needing manual correction. This preliminary automated action reduces the time manual reviewers must spend on each element while maintaining high accuracy through human validation of critical cases.
2Productivity
If machine learning models are used to predict class labels, then labeling speed is improved, but the complexity of evaluating and training the models increases
Solution Approach 1:
The system implements feedback mechanisms where manually corrected labels are fed back into the machine learning model for retraining, and where prediction confidence scores are displayed to reviewers. This feedback loop allows the model to learn from corrections and improve its predictions over time, reducing the complexity of manual evaluation while maintaining high labeling speed and accuracy.
3Stability of the object's composition
If consistent labeling standards are enforced across all teams, then labeling consistency is improved, but implementation difficulty and resistance increase
Solution Approach 1:
The system acts as an intermediary between different teams and organizations by providing a unified platform that enforces consistent labeling standards while accommodating different user interfaces and workflows. The machine learning model serves as an intermediary that translates varied input data into consistent class labels, reducing the friction of standard enforcement across diverse teams without requiring each team to adopt identical processes.
Data Source
AI summary
In example embodiments, techniques are provided for labeling elements of an infrastructure model with classes. The techniques may be implemented by a labeling tool that uses an ML model to create element selections and provides a cycle review mode to speed review within such selections. The labeling tool may further provide for two file loading and a number of visualization schemes to speed comparison of label files and prediction files.