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OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard
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| Definitionskos:definition | 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 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 How to introduce external classification schemes and flexible means for representing ground truth labeling. |
|---|---|
| Broaderbroader | Implementation Standard |
| http://purl.org/dc/terms/createdcreated | 2023-09-19 |
| Creatorcreator | Peng Yue, Boyi Shangguan |
| seeAlsoseeAlso | https://docs.ogc.org/is/23-008r3/23-008r3.html |
| Statusstatus | valid |
| Notationnotation | 23-008r3 |
| Alternative LabelaltLabel | 23-008r3 |
| OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard | |
| OGC document typedoctype | Implementation Standard |