Geographic information — Training data markup language for artificial intelligence — Part 1: Conceptual model

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).