Project description
Making AI in Earth Observation explainable
Satellite imagery is used for a range of purposes, including land-use planning and climate monitoring. However, while AI models help process vast datasets, their inner workings often remain a ‘black box’ – raising concerns about trust and reliability. Supported by the Marie Skłodowska-Curie Actions programme, the GEODE project is making deep learning models more transparent and accountable. Specifically, it will use cutting-edge explainable AI techniques to explore why models succeed or fail, where they perform well, and how data should be labelled for global mapping. By shedding light on AI’s decision-making process at the pixel level, the project aims to improve the credibility and usability of automated Earth observation tools worldwide.
Objective
The GEODE project brings explainability to deep learning for Earth observation. GEODE aims at improving the transparency of computer vision models used for analyzing satellite imagery in large-scale mapping projects. As AI models are increasingly used in applications such as environmental monitoring and land-use classification, their opaque decision-making processes limit trust and usability. Using novel XAI (eXplainable AI) techniques, GEODE will offer pixel-level explanations to understand why a given model fails or succeeds on certain objects. Performance maps will indicate where the model is expected to have standard performance. Finally, an active learning strategy will be developed, to define how to conduct labeling work for global mapping.
Answering the why, where and how deep mapping models work, GEODE is a bold step towards unlocking the full potential of AI paired with large volumes of satellite imagery. The project will take place at the Image & Signal Processing Laboratory at Universitat de València in Professor Gustau Camps-Valls' group, a world-renowned leader in machine learning for Earth observation.
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.
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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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.
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.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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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.
46010 Valencia
Spain
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