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Geospatial Artificial Intelligence Analysis for Tailings Storage Facilities

Project description

Geospatial prototype system for tailings storage facilities monitoring

Tailings storage facilities (TSFs) are dams designed to safely and permanently hold in finely ground rock particles, water and chemicals resulting from mineral and ore processing. These vital dynamic structures help control and prevent contamination of the environment, surrounding populations and water bodies by pollutants. The EU-funded GAIA-TSF project aims to develop a prototype system based on satellite Earth observation and machine learning to monitor TSFs. To operationalise this, researchers from the Czech Republic, the Netherlands, Portugal, South Africa, Spain and Zambia will work with mining operational communities to identify monitoring variables. The project will explore the integration of these technologies to automatically detect anomalies and risks in TSFs and to test various machine learning algorithms to improve monitoring capacity.

Objective

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.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-EUSPA-2023-SPACE

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Coordinator

CLUSTER PARA LA MINERIA SOSTENIBLE Y SERVICIOS ASOCIADOS DE LA PENINSULA IBERICA - IBERIAN SUSTAINABLE MINING CLUSTER
Net EU contribution
€ 216 468,75
Address
C/JULIA MORROS S/N OFICINA 107 PARQUE TECNOLOGICO ARMUNIA
24009 LEON
Spain
Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Other
Links
Total cost
No data

Participants (6)