Observations Signal & Environment (OSE) team

At ENSTA Bretagne, the researchers involved in Lab-STICC’s "Observations Signal & Environment" (OSE) team concentrate their expertise on signal processing and AI applied to the marine environment in order to develop and extract datasets relevant for measures protecting the marine environment.

Research areas for protecting the marine environment

To meet the environmental challenges and support stakeholders (NGOs, policy makers, civil society and the private sector) in their actions to minimize the impact of human activity on the marine environment, considerable effort has been invested over recent decades in increasing observations of the marine environment. Accordingly, worldwide, more than 90% of environmental data has been generated in the last three years (signals, images and time series).

Against this backdrop, artificial intelligence (AI) techniques, not least data science and machine learning, have an instrumental role to play in analyzing and processing data relevant for measures protecting the marine environment.

The "OSE" research team has taken up this methodological research in signal processing and AI applied to the marine environment, by focusing more particularly on multimodal remote sensing:

oceanic remote sensing (satellite images),
monitoring of the marine environment (aerial images, GNSS, ARGOS or AIS data, etc.)
and underwater monitoring (passive acoustics, underwater video images).

One of the team’s concerns is to improve the performance of AI algorithms through in-depth consideration of the nature of the phenomena we wish to find out more about.

Three aspects of the interface between AI and monitoring of the marine environment are addressed: AI & physics, AI & robustness and AI & inverse problems. The latter, for example, includes building AI strategies on the basis of large datasets to resolve environmental inverse problems (data interpolation, target recognition, geophysical signal inversion, information reduction, etc.).

Examples of applications

Geophysical dynamics at the ocean surface: by variational model and solver learning for data assimilation.
Recognition of fish species in underwater video images.

Collaborations

Companies: Thales, NavalGroup, CLS, Eodyn, Actimar, Hytech-imaging.
Institutions: CNES, DGA, IFREMER, IUEM, INRIA, Météo France, Mercator Ocean, IRISPACE, EUR ISblue, SHOM, FEM.
Academia: GIPSA, Marbec, Sorbonne Univ, UCLA (Computational and Applied Mathematics group), Univ. of Washington (Applied Phys. Lab., Applied Math. Dept), Australian Antarctic Division, Barcelona Supercomputer Center, NERSC, Univ. of Dalhousie (Institute of Big Data Analytics), IMEDEA (Spain), Univ. Laval (Canada), ETH Zurich, Univ. Buenos Aires (CIMA, Argentina).
and the GDR ISIS research network (CNRS)