An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data

Fahimeh Farahnakian, Mahyar Yousefi, Ana Cláudia Teodoro

2025

Abstract

Traditional mining exploration techniques require significant effort, including drilling and sample collection, making the process highly challenging and costly. The application of machine learning (ML) in mineral exploration has revolutionized the field by improving efficiency and accuracy in identifying critical raw materials (CRM). This study presents a novel framework that integrates Light Detection and Ranging (LiDAR) and PRISMA hyperspectral data with ML techniques to enhance mineral exploration. By leveraging an ensemble model combining Random Forest (RF) and Multi-Layer Perceptron (MLP), this approach captures complex spatial and spectral patterns, improving the prediction of cobalt, copper, and nickel concentrations. To address the challenge of limited labeled data, synthetic samples were generated using the Gaussian Copula Synthesizer (GCS), enhancing model generalization. The proposed methodology was validated at the ´Aramo mine in Asturias, Spain, demonstrating that the fusion of multispectral and topographical features significantly improves predictive accuracy. The results show that the scalability and robustness of this framework for identifying CRM in geologically significant yet underexplored regions.

Paper Citation

in Harvard Style

Farahnakian F., Yousefi M. and Teodoro A. (2025). An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I; ISBN 978-989-758-741-2, SciTePress, pages 286-296. DOI: 10.5220/0013493300003935

in Bibtex Style

@conference{s34i25,
author={Fahimeh Farahnakian and Mahyar Yousefi and Ana Teodoro},
title={An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I},
year={2025},
pages={286-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013493300003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I
TI - An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data
SN - 978-989-758-741-2
AU - Farahnakian F.
AU - Yousefi M.
AU - Teodoro A.
PY - 2025
SP - 286
EP - 296
DO - 10.5220/0013493300003935
PB - SciTePress