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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).
Reģistrācijas numurs (WIID)
80117
Darbības sfēra
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).
