Artificial intelligence applied to mapping of the seafloor and marine spatial planning
ACRONYM
KIMERA
Title
Artificial intelligence applied to mapping of the seafloor and marine spatial planning
General information
So far and despite all efforts only about 25 % of the ocean floor are mapped using hydroacoustic methods. This means that we know very little about the nature of the remaining 75 %. This knowledge is based on satellite measurements of the Earth's gravity and the derivation of seafloor morphology from that data. However, the comparison of satellite derived data to hydroacoustic data reveals that quite often there are major mismatches in the resulting digitial elevation models in all three dimensions. Also the knowledge about seafloor morphology is only a first steps towards an understanding of its nature. Predictions on the probability for mineral potentials or the presence of habitats linked to specific oceanographic parameters can be derived only from here. And this knowledge is key for combining the need for conservation and restoration of the oceans on the one and allowing for a sustainable use on the other hand.
This is were the 'KIMERA' projects starts. KIMERA is the German acronym for 'Artificial intelligence applied to mapping of the seafloor and marine spatial planning'. Within the 36 months-long duration of the project, a method for the predictive mapping (RPM) of the nature of the seafloor using machine learning and artificial intelligence will be developed in collaboration with a regional private entity. This information will then guide the prediction of certain habitats or mineral potentials. This is a first important step towards marine spatial planning based on scientific evidence, which needs future ground-truthing.
Start
September, 2023
End
January, 2027
Funding (total)
798000
Funding (GEOMAR)
798000
Funding body / Programme
-
Helmholtz-Gemeinschaft
/
Other
/ Land Schleswig-Holstein / WTSH
Coordination
Helmholtz-Zentrum für Ozeanforschung Kiel (GEOMAR), Germany
Contact
Partners
north.io
