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

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Farahnakian, F (Author) Other; Yousefi, M (Author) Other; Ana Teodoro (Author) FCUP

Conference proceedings International

Title: Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management

Pages: 286-296

11th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2025

Porto, 1 April 2025 through 3 April 2025

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-018-QNF

DOI: 10.5220/0013493300003935

Abstract (EN): 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 Áramo 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. © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10