Decision tree machine learning algorithm for pegmatites mapping using remote sensing data (Anti-Atlas, Morocco)

In the past few years, the use of Machine learning (ML) to classify remotely sensed data has increased, offering new opportunities for geological mapping. Conventional remote sensing classification methods often rely on spectral information, but distinguishing between lithological classes with simil...

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Vydané v:	Applied geomatics Ročník 17; číslo 3; s. 535 - 546
Hlavní autori:	Maimouni, Soufiane, Morsli, Yousra, Zerhouni, Youssef, Alikouss, Saida, Baroudi, Zouhir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Predmet:	Algorithms Classification Decision trees Earth and Environmental Science Geographical Information Systems/Cartography Geography Geologic mapping Geological mapping Geology Geophysics/Geodesy Granite Heterogeneity Learning algorithms Lithology Machine learning Mapping Measurement Science and Instrumentation Mineral exploration Mineralogy Outcrops Pegmatite Radiometers Remote sensing Remote Sensing/Photogrammetry Spectral signatures Surveying Thermal emission Morocco Pegmatite mapping Angarf Spectroradiometric measurements ASTER Decision tree
ISSN:	1866-9298, 1866-928X
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Popis
Shrnutí:	In the past few years, the use of Machine learning (ML) to classify remotely sensed data has increased, offering new opportunities for geological mapping. Conventional remote sensing classification methods often rely on spectral information, but distinguishing between lithological classes with similar spectral signatures remains a persistent challenge. In particular, accurately mapping and extracting pegmatites from other lithological classes, especially granite, presents a difficulty. The objectives of this study are to map the lithological units in the Angarf region (Zenaga, Central Anti-Atlas, Morocco) and to extract pegmatite outcrops, with a particular focus on separating the pegmatite from the granite, as this challenge has been considered in several previous studies. The methodology developed is innovative and based on a Decision Tree (DT) approach of ML, which is applied to spectral indices derived from ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) images. The interpretation and analysis of spectroradiometric measurements have enabled us to understand the behavior of spectral information of pegmatites compared to other geological formations. The achieved overall accuracy of the DT classification was 96.28 %. Also, the comparison of the produced map, particularly the pegmatite classes, with the field data highlighted the potential of the adapted methodology. The DT algorithm is a fast, reliable, robust, and highly accurate mapping model that is simple to configure, uses few parameters, and handles input data heterogeneity effectively. The obtained pegmatite maps provide a support and can be used as a preliminary step in mineral exploration.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1866-9298 1866-928X
DOI:	10.1007/s12518-025-00633-7