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GAIA-TSF

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Geospatial Artificial Intelligence Analysis for Tailings Storage Facilities

The Geospatial Artificial Intelligence Analysis for Tailings Storage Facilities (GAIA-TSF) aims to design and develop the prototype of a system based on satellite earth observation and machine learning algorithms to achieve contin... see more

28/02/2027

unknown leader

998K€

Project Budget: 998K€

Project leader unknown leader

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-10-01

HORIZON-EUSPA-2023-SPACE-01-61: EU GOVSATCOM for a safer and more s... Expected Outcome:Projects are expected to contribute to the following outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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No hay información privada compartida para este proyecto. Habla con el coordinador.

7 Participantes

CLUSTER PARA LA MINERIA SOST...

UNIVERZITA KARLOVA

TECHNISCHE UNIVERSITEIT DELF...

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Project duration: 28 months Date Start: 2024-10-01
End date: 2027-02-28

Project leader unknown leader

Project Budget 998K€

participation deadline Sin fecha límite de participación.

Project description The Geospatial Artificial Intelligence Analysis for Tailings Storage Facilities (GAIA-TSF) aims to design and develop the prototype of a system based on satellite earth observation and machine learning algorithms to achieve continuous multi-level/multi-scale characterisation and monitoring of Tailings Storage Facilities (TSFs). To achieve this goal, GAIA-TSF consortium will engage in interdisciplinary and international collaborative research and development, integrating the fields of geoscience, Earth Observation (EO), and Machine Learning (ML) Science as well as five countries (Spain, Portugal, Netherlands, Czech Republic, Zambia and South Africa). The project will focus on establishing strong interactions with user communities (mining authorities and mining industry) involved in TSF operational activities with the aim of defining clear customer-specific functional and technical requirements. These requirements will lay down Key Variables (KV) that could be used as parameters to monitor TSF as well as precise performance objectives for the GAIA-TSF prototype. On this foundation, the cooperative work between scientists and the mining operational communities will lead to explore how three technical disciplines, namely satellite earth observation, geo-engineering and machine learning, can be integrated into the design of a prototype supporting the automatic detection of anomalies and risks in TSFs. Based on the designed variables and structured training datasets, different machine and deep learning algorithms will be tested and evaluated to design and develop a prototype supporting the automatic detection and prediction of anomalies and risks in TSFs.

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