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.

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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.

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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

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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.

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AIREO activity

Artificial Intelligence has great potential to advance processing and analysis of Earth Observation (EO) data. Training datasets (TDS) are crucial for AI applications but they are becoming a major bottleneck in more widespread and systematic application of machine learning in EO. AIREO provides resources and tools to data creators and users to ensure their TDS are FAIR and to standardise aspects of TDS such as quality assurance and metadata completeness indicators.

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Community activity

In order to engage with the EO, AI and related stakeholder communities for gathering existing activities, standards, methods, tools and requirements, as well as to drive the development of the AIREO specifications and the best-practices, the AIREO Network with over 100 participating organisations was set up. This network is constantly growing and is being engaged through a number of channels presented here. If you would like to join the AIREO Network, please email aireo@info.net.

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AIREO Resources

AIREO Training Dataset Specification

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AIREO Training Dataset Best Practice Guidelines

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AIREO Training Dataset Pilot Datasets

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AIREO Python Library

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AIREO activity

Community activity

News

Nasa Space Apps Challenge 2021 Announced

SpaceNet Challenge 7 Results and Winning Models Released

AIREO Presentation at the European Geophysical Union Assembly, 2021

The first release version of the STAC specification is published

AIREO Phi-Week Event

AIREO flyer published

Contacts