4 documents found, page 1 of 1
S34I Project: Secure and Sustainable Supply of Raw Materials for EU Industry
Ana Teodoro; Cardoso Fernandes, J; Gheorghe, M; Falabella, F; Calò, F; Pepe, A; Hanelli, D; Knobloch, A; La Rosa, R; Farahnakian, F; Georgalas, GPThe Secure and Sustainable Supply of Raw Materials for EU Industry - S34I project is researching and innovating new data-driven methods to analyze Earth Observation (EO) data, supporting systematic mineral exploration and continuous monitoring of extraction, closure, and post-closure activities to increase European autonomy regarding raw materials (RM) resources, and to use EO not only for the management of tec...
Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping
La Rosa, R; Steffen, M; Storch, I; Knobloch, A; Cardoso Fernandes, J; Carvalho, M; Barrios, MS; Sánchez Migallón, JM; Nygren, P; Williams, VTo meet the European Unions growing demand for critical raw materials in the transition to green energy, this study presents a novel, cost-effective, and non-invasive methodology for mineral prospectivity mapping. By integrating hyperspectral data from satellite, airborne, and ground-based sources with deep learning techniques, we enhance mineral exploration efficiency. We employ Bayesian Neural Networks (BNNs)...
Stream sediment analysis for Lithium (Li) exploration in the Douro region (Port...
Cardoso-Fernandes, J; Lima, J; Alexandre Lima; Roda-Robles, E; Kohler, M; Schaefer, S; Barth, A; Knobloch, A; Goncalves, MA; Goncalves, F; Ana TeodoroLithium (Li) was recently added to the list of critical raw materials by the European Union due to its significance for the green energy transition. Thus, the development of new toolchains to make Li exploration more economic and more effective is needed. Stream sediment analysis can play an important part in these new tool chains. In this work, two historical stream sediment datasets covering parts of the Freg...
Lithium Potential Mapping Using Artificial Neural Networks: A Case Study from C...
Koehler, M; Hanelli, D; Schaefer, S; Barth, A; Knobloch, A; Hielscher, P; Cardoso Fernandes, J; Alexandre Lima; Ana TeodoroThe growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification...