Detaljer om förslaget

Vienna agreement ISO lead

Training data is the building block of machine learning models. These models now constitute the majority of machine learning applications in Earth science. Training data is used to train AI/ML models, and to then validate model results. Formalizing and documenting the training data by characterizing the training data content, metadata, data quality, and provenance, and so forth is essential. This document describes work actions around training data: — Documents the UML model with a target of maximizing the interoperability and usability of EO imagery training data; — Defines different AI/ML tasks and labels in earth observation in terms of supervised learning, including scene level, object level and pixel level tasks; — Describes the description of the permanent identifier, version, license, training data size, measurement or imagery used for annotation, and so on; — Defines the description of quality (e.g. training data errors, training data representativeness) and the provenance (e.g. agents who perform the labeling, labeling procedure).