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NBN EN ISO 19178-1:2025
Geographic information - Training data markup language for artificial intelligence - Part 1: Conceptual model (ISO 19178-1:2025)
Summary
Within the context of training data for Earth Observation (EO) Artificial Intelligence Machine Learning (AI/ML), this document specifies a conceptual model that:
— establishes a UML model with a target of maximizing the interoperability and usability of EO imagery training data;
— specifies different AI/ML tasks and labels in EO in terms of supervised learning, including scene level, object level and pixel level tasks;
— describes the permanent identifier, version, licence, training data size, measurement or imagery used for annotation;
— specifies a description of quality (e.g. training data errors, training data representativeness, quality measures) and provenance (e.g. agents who perform the labelling, labelling procedure).
— establishes a UML model with a target of maximizing the interoperability and usability of EO imagery training data;
— specifies different AI/ML tasks and labels in EO in terms of supervised learning, including scene level, object level and pixel level tasks;
— describes the permanent identifier, version, licence, training data size, measurement or imagery used for annotation;
— specifies a description of quality (e.g. training data errors, training data representativeness, quality measures) and provenance (e.g. agents who perform the labelling, labelling procedure).
Technical characteristics
| Publisher | Bureau de Normalisation Belge (NBN) |
| Publication Date | 06/05/2025 |
| Page Count | 0 |
| EAN | --- |
| ISBN | --- |
| Weight (in grams) | --- |
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