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.

Focus on Africa Space Science and Technology 4 Future development (FAST4Future) aims at strengthening Science, Technology, Engineering and Mathematics (STEM) in Africa Higher education by promoting Planetary and Space Science and Technology (PSST) for the sustainable growth and jobs creation in a continent that is rapidly embracing the 4th industrial revolution. PSST has been identified as key area of investment in Africa as it provides graduates and young scientists with both the necessary soft and practical skills to face 21st century challenges such as digital innovation and job creation. The aims of FAST4Future are: i) To increase the accessibility to STEM in African Higher Education (HE); ii) To modernize existing PSST programmes in collaboration with industry and policy makers; iii) To foster the internationalization of partner HEIs by promoting mobility of staff; iv) to promote standardization of PSST in Africa in support of students’ mobility. To achieve these ambitious goals FAST4future will promote the creation of a Centre of Excellence in PSST (CESST) linked to a virtual platform to increase the accessibility coordinate collaborative actions including: i) analysis of the conditions of women in STEM; ii) review existing PSST programs in order to fill the mismatches between academic offer and job market requirements; iii) draft a white paper for the credit recognition of PSST courses across African institutions; iv) train administrative staff and facilitate the mobility of academic staff; v) promote the transfer of skills in key strategic areas of PSST to boost the employability of graduate students. The creation of a virtual platform within CESST will open new pathways for dissemination of high quality academic contents that can potentially reach thousands of STEM students across the continent and facilitate the access to STEM disciplines for Africans living in remote areas, conflict zones and will, eventually, improve the number of women in STEM.