OGC Approves Model for Underground Data Definition and Integration as Official Standard
MUDDI serves as a framework to make different datasets for underground objects interoperable, exchangeable, and more easily manageable.
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:
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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