Best Paper Award
| Refaey Elwardany |
| Affiliation |
Al-Azhar University |
| Country |
Egypt |
| Scopus ID |
57344533900 |
| Documents |
18 |
| Citations |
312 |
| h-index |
8 |
| Subject Area |
Earth and Planetary Sciences |
| Event |
Best Paper Awards |
| ORCID |
0000-0001-6766-6921 |
Refaey Elwardany
Refaey Elwardany of Al-Azhar University, Egypt, is recognized in connection with the Best Paper Award for research on remote sensing, machine learning, mineral exploration, and geological mapping. His research publication, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” investigates the integration of multispectral remote sensing, mineralogical analysis, and machine learning for delineating gold-sulfide mineralization in the Fatira mine area. The article was published in Acta Geologica Sinica (English Edition), volume 99, issue 4, pages 1196–1223, in 2025, with DOI 10.1111/1755-6724.15333. [1]
Abstract
The Best Paper Award recognition highlights research by Refaey Elwardany concerning the application of remote sensing and machine learning techniques to gold exploration in the Fatira (Abu Zawal) mine area of Egypt. The study integrates fieldwork and mineralogical analysis with Landsat-8 OLI, ASTER, and Sentinel-2 multispectral imagery to delineate gold-sulfide mineralization and associated hydrothermal alteration. Principal component analysis, independent component analysis, supervised classification, and mineral indices were applied to identify alteration zones and geological features associated with mineralization. The reported Landsat-8 support vector machine classification achieved an accuracy of 88.55% with a Kappa value of 0.86. [1]
Keywords
Remote Sensing, Machine Learning, Gold Exploration, Gold-Sulfide Mineralization, Fatira Gold Mine, Hydrothermal Alteration, Mineralogy, Landsat-8, ASTER, Sentinel-2, Geological Mapping, Egypt.
Introduction
Remote sensing has become an important component of modern geological investigation because multispectral satellite observations can support the identification and spatial interpretation of lithological and hydrothermal alteration features. In mineral exploration, the integration of remotely sensed data with field observations, mineralogical information, and computational classification methods can provide a systematic approach for mapping prospective zones. The Fatira mine area, located in the northern Eastern Desert of Egypt, represents a geological setting in which remote sensing and machine learning techniques can contribute to the characterization of gold-sulfide mineralization. [1]
Research Profile
Refaey Elwardany is affiliated with Al-Azhar University in Egypt and is associated with research in Earth and Planetary Sciences. The supplied academic profile records 18 documents, 312 citations, and an h-index of 8. These metrics provide a quantitative representation of the researcher’s publication and citation activity as specified for this recognition profile.
Research Contributions
The principal contribution of the awarded study is the integration of multisource remote sensing data and machine-learning methods for mapping gold-mineralized alteration zones in the Fatira mine area. Fieldwork and mineralogical analysis were combined with satellite imagery from Landsat-8 OLI, ASTER, and Sentinel-2 to identify geological and alteration characteristics associated with gold-sulfide mineralization. [1]
Publications
The principal publication associated with this recognition is the following peer-reviewed journal article:
- El-Wardany, R., Jiao, J., Zoheir, B., Khedr, L., Kumral, M., Liu, L., Abu El-Leil, I., Orabi, A., Abd El-Salam, L., & Abdelnasser, A. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition), 99(4), 1196–1223. https://doi.org/10.1111/1755-6724.15333. [1]
Research Impact
The research contributes to the field of mineral exploration by demonstrating how satellite-based observations can be integrated with machine-learning classification and geological analysis to identify alteration zones associated with gold mineralization. Its methodological framework is relevant to geological mapping and exploration studies in areas where conventional field investigation may benefit from spatially extensive remote sensing data. [1]
Award Suitability
The Best Paper Award is intended to recognize research demonstrating scholarly quality, methodological rigor, originality, and relevance within its field. The publication associated with Refaey Elwardany addresses a defined geological exploration problem through the integration of remote sensing, mineralogical analysis, and machine-learning techniques. Its use of Landsat-8, ASTER, and Sentinel-2 data, together with quantitative classification and alteration mapping, provides a clearly structured methodological contribution. [1]
Conclusion
Refaey Elwardany’s research profile, as presented for the Best Paper Award, reflects scholarly activity in Earth and Planetary Sciences with a particular emphasis on geological mapping, remote sensing, mineral exploration, and machine learning. The recognized publication presents an integrated approach to mapping gold-mineralized alteration zones in the Fatira mine area of Egypt using multispectral satellite imagery, geological observations, mineralogical analysis, and computational classification techniques. [1]
External Links
References
- El-Wardany, R., Jiao, J., Zoheir, B., Khedr, L., Kumral, M., Liu, L., Abu El-Leil, I., Orabi, A., Abd El-Salam, L., & Abdelnasser, A. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition), 99(4), 1196–1223. DOI: https://doi.org/10.1111/1755-6724.15333.
- Wiley Online Library. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition). Publisher Article Page.
- Istanbul Technical University. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Research publication record. Publication Record.