AIREO Resources

AIREO Training Dataset Specification

FAIR (findable, accessible, interoperable and re-usable) data principles are at the heart of this specification, which provides a common structure for EO Training Datasets. Innovations for fairifying data include documentation of data provenance, proposed standardised quality indicators, automation of quality indicator checking and the introduction of AIREO Compliance Levels to rapidly assess the maturity and completeness of a dataset.

AIREO Training Dataset Best Practice Guidelines

The AIREO Best Practice Guidelines outline how to generate and document AIREO-compliant datasets following the AIREO specifications. The  guidelines  consider  best  practice  from  both  the  EO  and  AI/ML  communities, as well as specific  recommendations  relevant  to  the  AIREO  specifications. The innovations introduced in the AIREO specification are described in more detail in the Guidelines from a data providers perspective.

AIREO Training Dataset Pilot Datasets

Four pilot datasets are provided for users to demonstrate the AIREO innovations in practical terms. Each dataset is accompanied by a Jupyter Notebook using the AIREO Python Library functionality. 
• AI4Arctic Automated Sea Ice Products dataset
• Common Agricultural Practice (CAP) Austria dataset
• Forest Observation System (FOS) dataset
• Spacenet7 Dataset

AIREO Python Library

The AIREO Python library is being developed to support users in creation and application of AIREO-compliant datasets. For the initial version, basic functionality is provided allowing loading and exploring the pilot datasets as well as populating critical metadata and running automated checking.

AIREO Jupyter notebooks

The Jupyter notebooks allow each of the pilot datasets to be explored from the perspective of both a data creator and a data user. The functionality of the Python library is demonstrated in the notebooks.