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In this research, airborne geophysical and remote sensing datasets were integrated for gold potentiality mapping (GPM) over the Atalla area in Central Eastern Desert, Egypt. Utilizing aeromagnetic data, detailed structural complexity maps were constructed using the center for exploration targeting (CET) procedure. Then, spectrometric gamma-ray data primarily located hydrothermally altered tracts with discriminating various rock units. The latter are precisely outlined by implementing various techniques (false-color composite (FCC), band ratio (BR), relative absorption band depth (RBD), directed principal component analysis (DPCA), and constrained energy minimization (CEM)) to ASTER, Sentinel 2 and ALOS PRISM datasets, with reference to the geological maps. The study exhibits that gold mineralization is structurally controlled by NW-SE direction. The findings of structural complexity and hydrothermal alteration (argillic, advanced argillic, phyllic, and propylitic) were used as weighted inputs for contouring gold potentiality. The resultant GPM accentuated five gold-promising zones; two are confirmed via locations of ancient gold mines, while the remaining three zones are strongly recommended for their gold potentiality.

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Different types of remote sensing data are commonly used as inputs for lithological classification schemes, yet determining the best data source for each specific application is still unresolved, but critical for the best interpretations. In addition, various classifiers (i.e., artificial neural network (ANN), maximum likelihood classification (MLC), and support vector machine (SVM)) have proven their variable efficiencies in lithological mapping, yet determining which technique is preeminent is still questionable. Consequently, this study aims to test the potency of Earth observing-1 Advanced Land Imager (ALI) data with the frequently utilized Sentinel 2 (S2), ASTER, and Landsat OLI (L8) data in lithological allocation using the widely accepted ANN, MLC, and SVM, for a case study in the Um Salatit area, in the Eastern Desert of Egypt. This area has a recent geological map that is used as a reference for selecting training and testing samples required for machine learning algorithms (MLAs). The results reveal (1) ALI superiority over the most commonly used S2, ASTER, and L8; (2) SVM is much better than MLC and ANN in executing lithologic allocation; (3) S2 is strongly recommended for separating higher numbers of classes compared to ASTER, L8, and ALI. Model overfitting may negatively impact S2 results in classifying small numbers of targets; (4) we can significantly enhance the classification accuracy, to transcend 90% by blending different sensor datasets. Our new approach can help significantly in further lithologic mapping in arid regions and thus be fruitful for mineral exploration programs.

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