An OGC TrainingDML-AI approach for making EO training datasets ready in deep learning frameworks

This is the authors' abstract. We don't add key points for this paper.

Training data (TD) are vital for artificial intelligence deep learning or machine learning (AI DL/ML) in remote sensing image interpretation. With the proliferation of a large number of Earth Observation (EO) datasets, the availability of a wide range of datasets has introduced challenges in ensuring the FAIR (Findable, Accessible, Interoperable, Reusable) use of training datasets. This paper proposes an approach that leverages the OGC Training Data Markup Language for AI (TrainingDML-AI) standard to make training data ready to be consumable by existing DL frameworks. It presents a training data pipeline approach to integrate TD in DL. The approach enables the retrieval and transformation of training data for compatibility with existing deep learning frameworks.