Objective
The NEUROSAT proposal is devoted to the processing of environmental observing satellite data with Neural Networks. A large number of potential users is expected. NEUROSAT is relevant of theme 3 (Area 3.1.1) of the E.C. Environment and Climate programme.
NEUROSAT will concentrate attention on modelling complex transfer (direct and inverse) satellite sensor functions and analysing large climatological satellite data sets with Neural Networks.
Preliminary studies have shown the relevancy of Neural Networks in processing satellite data thanks to there ability to model complex functions. First, it is proposed to utilise Neural Networks to improve the speed, the accuracy and the flexibility of the algorithms to compute the sea surface wind from ERS1 and N.SCAT scatterometers, the atmospheric integrated vapour content from SSM/I sensor, the atmospheric profiles from NOAA/TOVS sensor and the forthcoming high resolution spectral IASI sensor.
Neural Network methodology will also be developed to analyse the structure pattern of clouds and their evolution. Neural Network algorithms are expected to be used by Meteorological Agencies dealing with numerical weather predictions to obtain more accurate satellite data and process them in real time.
Secondly, Neural Network methodology will be developed to improve the efficiency of the analyses of large climatological satellite data base and to extract relevant parameters related to climatic change. NEUROSAT will contribute in providing pertinent information to the different teams involved in climatic studies. In that sense NEUROSAT is related to the forthcoming climate research programme CLIVAR.
We also propose to investigate the role of Neural Networks in forecasting the ocean state from satellite data such as sea surface temperatures and altimetry. Attention will be focused on the Morocco upwelling. Neural Networks may lead to simple and flexible models much more easy to handle on small computer than OGCM using accurate assimilating scheme. NEUROSAT will be useful for coastal management and fisheries specially for small and developing countries. The Neural Network methodology is a new methodology and is a good example of co-operation between geophysicists and applied mathematicians involved in artificial intelligence.
NEUROSAT will contribute to promote an original and efficient European methodology in satellite remote sensing.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences earth and related environmental sciences atmospheric sciences meteorology
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
- natural sciences earth and related environmental sciences atmospheric sciences climatology climatic changes
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
75252 PARIS
France
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