Training Data Markup Language for Artificial Intelligence

This standard aims to develop the unified modeling language (UML) model and encodings for geospatial machine learning training data. Training data plays a fundamental role in Earth Observation (EO) Artificial Intelligence Machine Learning (AI/ML), especially Deep Learning (DL). It is used to train, validate, and test AI/ML models.

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Document title Version OGC Doc No. Type
OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard 1.0 23-008r3 IS

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Overview

The Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Standard aims to develop the UML model and encodings for geospatial machine learning training data. Training data plays a fundamental role in Earth Observation (EO) Artificial Intelligence Machine Learning (AI/ML), especially Deep Learning (DL). It is used to train, validate, and test AI/ML models. This Standard defines a UML model and encodings consistent with the OGC Standards baseline to exchange and retrieve the training data in the Web environment.

The TrainingDML-AI Standard provides detailed metadata for formalizing the information model of training data. This includes but is not limited to the following aspects:

  • How to introduce external classification schemes and flexible means for representing ground truth labeling.
  • How the training data is prepared, such as provenance or quality;
  • How to specify different metadata used for different ML tasks such as scene/object/pixel levels;
  • How to differentiate the high-level training data information model and extended information models specific to various ML applications; and