Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

USING GIS DATA AND MACHINE LEARNING FOR MINERAL MAPPING. STUDY CASE, BOU SKOUR EASTERN ANTI-ATLAS, MOROCCO

Domain:

geospatial

Record type:

paper
Creator:
N. H. M. A.
Publisher:
Cop
Host:
Abstract. The continued demand for mineral deposits in recent years has led exploration geologists for each stage of mineral exploration; find more effective and innovative ways of processing different data types. The use of Geographic Information Systems (GIS) allows various features, such as elevation, slope, tectonic structures, lithological units and indicator minerals of Bou Skour region, Eastern Anti-Atlas, Morocco to be mapped making targeted mining decisions easier. In this paper, a methodology was developed to enable the automated mapping of mineral using machine learning methods such Random Forest (RF) and Artificial Neural Network (ANN) achieves approximately 98% classification accuracy on a single Intel® Core™ i5-5300U CPU core with 16GB of memory, and come up with predictive maps representing the probable potentially mineralized areas.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, MoroccoIntegrating Automated Lineament Extraction, Magnetic Data, and Machine Learning-Based Lithological Mapping in the Anti Atlas, MoroccoMapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)Integrated Sentinel-2 and Landsat-9 data for high-resolution lithological mapping in the Rich High Atlas (Morocco) using machine learning and deep learningEvaluation of machine learning algorithms for forest species mapping based on Sentinel 2 data: a case study of Ait Bouzid forest (Central High Atlas, Morocco)Advanced Drought Prediction Using Hybrid Deep Learning Models: A Case Study of the High Atlas and Anti-Atlas Mountains

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Usi

Integrating Automated Lineament Extraction, Magnetic Data, and Machine Learning-Based Lithological Mapping in the Anti Atlas, Morocco

   AbstractThis study explores advanced remote sensing, geophysical, and geospatia

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

This research work is intended as a contribution to the development of a multicriteria methodology,

Integrated Sentinel-2 and Landsat-9 data for high-resolution lithological mapping in the Rich High Atlas (Morocco) using machine learning and deep learning

Evaluation of machine learning algorithms for forest species mapping based on Sentinel 2 data: a case study of Ait Bouzid forest (Central High Atlas, Morocco)

In arid and semi-arid environments, producing accurate maps of forest tree cover using optical remot

Advanced Drought Prediction Using Hybrid Deep Learning Models: A Case Study of the High Atlas and Anti-Atlas Mountains

Abstract. Morocco’s High Atlas and Anti-Atlas mountains have faced escalating drought severity in re