Interoperable machine learning for Earth observation and climate in federated cyberinfrastructures

Tom Landry1

1Computer Research Institute of Montreal

 

Earth Observations (EO) enable scientific research, such as study of meteorology and climate, ecosystems and forests, hydrology and marine life. Machine Learning (ML) techniques are key to solve these complex multidisciplinary problems. ML comes with difficulties associated with managing and processing large datasets of heterogeneous data in increasingly distributed infrastructures. This is the case for EO data produced by Copernicus Sentinel missions, as well as CMIP6 climate data managed by the Earth System Grid Federation (ESGF).

Aside from the raw data, researchers also need access to annotated training data and services to tune, re-train, discover and run ML models. New formats such as Open Neural Network Exchange (ONNX) enables model sharing between different Deep Learning frameworks. Software containers can be used to package and deploy algorithms and frameworks into standardized services, applications and workflows. An example of this is European Space Agency (ESA) Thematic Exploitation Platform (TEP) open architecture, recently advanced standardisation and best practices using Open Geospatial Consortium (OGC) Web Services (OWS).


Biography:

Tom possesses more than 20 years of experience in a variety of computer science fields, including E-Learning, geomatics, industrial automation, E-Commerce and sports science. His main interests are software architectures, project management, open innovation, remote sensing, big data and machine learning. As a product manager for geospatial platforms at CRIM, Tom leads several applied research projects joining Earth observation and climate. He is a member of CANARIE’s technical advisory committee and CRIM’s official liaison with the Open Geospatial Consortium (OGC). Since 2016, he has been involved in the Earth System Grid Federation (ESGF) as a member of its executive committee.

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