Project description
Deep learning in earth observation for better data
Earth observation (EO) is changing considerably because of the large amounts of observations obtained from remote sensing and in-situ sensor networks that acquire very precise localised measurements. Novel solutions are needed to obtain data from spaceborne and ground-based instruments for estimating geophysical parameters. To better understand multisource EO data, the EU-funded CALCHAS project will gather observations from different sources, combine sampling scales associated with spaceborne and in-situ measurements and analyse time series of dynamic observations. Mathematical tools will be used to extend the present capacity of single-source data analysis. The project will analyse time series of measurements from active and passive microwave and multispectral spaceborne imaging instruments, and in-situ sensor measurements.
Objective
Earth Observation (EO) is undergoing a radical transformation due to the massive volume of observations acquired by remote sensing and in-situ sensor networks. While satellites provide coarse-resolution, yet global-scale monitoring of environmental processes, in-situ sensor networks acquire high-accuracy localized measurements. Extracting information from spaceborne and ground based instruments requires innovative solutions which will allow the autonomous integration of diverse in nature and scale observations in order to provide high-quality geophysical parameter estimation. CALCHAS will demonstrate cutting edge technologies targeting three major factors towards the vision of fully automated multi-source EO data understanding, namely (i) the fusion of observations from different sources and modalities, (ii) the efficient aggregation of the sampling scales associated with spaceborne and in-situ measurements, and (iii) the analysis of time-series of dynamic observations. To that end, the paradigm-shifting signal processing and learning framework of Deep Learning will be utilized and extended through powerful mathematical tools and appropriate methodologies like supervised and generative learning, dramatically extending the current scope of single source data analysis. The developed framework will be employed for analyzing time-series of measurements from active and passive microwave and multispectral spaceborne imaging instruments (SMAP, SMOS and Sentinels), and in-situ sensor measurements, targeting the high-accuracy spatial and temporal resolution enhancement for observations and soil moisture estimation. The merits of the developed technology will be demonstrated in two intelligent water management case studies, namely optimized irrigation management and water pipeline leakage detection.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences data science
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors smart sensors
- engineering and technology environmental engineering remote sensing
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
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.
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.
Funding Scheme
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.
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.
MSCA-IF-GF - Global Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2018
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
70 013 IRAKLEIO
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.