Accurate landslide susceptibility mapping (LSM) is critical to risk management, especially in areas with significant development. Although the receiver operating characteristic–area under the curve (ROC–AUC) performance metrics are commonly used to measure model effectiveness, showed that these are not enough to check the reliability of the generated maps. In this study, the effectiveness of three machine learning models—logistic regression (LR), random forest (RF), and support vector machine (SVM)—were evaluated and compared in predicting landslide risk in a hilly region east of Cairo, Egypt. A comprehensive dataset was gathered to achieve that, including 183 landslide and 183 non-landslide locations, which were detected through fieldwork and high-resolution satellite imagery. Fourteen conditioning factors from different categories; topographical, geological, hydrological, anthropological, and trigger-related variables, were used as independent factors during the generation of the different LSM. All three models achieved high ROC–AUC values, with RF scoring 0.95, SVM 0.90, and LR 0.88, indicating strong performance. However, further assessment with additional performance metrics like accuracy (ACC), recall, precision, F1 score, and check rationality of the maps revealed key differences. Among the models, only the RF model appeared as the most reliable, with superior across all performance metrics, and fewer misclassifications in critical areas. In contrast, SVM and LR exhibited higher misclassification rates for both landslide-prone and safe locations. These findings show that high ROC–AUC values do not always equate to practical reliability.

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.

In the field of mineral exploration, it is strikingly evident that radioactive-bearing mineralization predominantly resides within granitic intrusions and along structural discontinuities. Consequently, the comprehensive mapping of lithological features emerges as a crucial means of accurately guiding the identification of these mineralizations. The present research is dedicated to enhancing the characterization of granitic rocks located within Egypt’s Central Eastern Desert (El-Missikat and El-Erediya regions). These areas have garnered attention due to their notably high concentrations of radioactive mineralizations, prompting the need for a more in-depth investigation. Despite the study area’s importance for potential radioactive mineral deposits, examining the geological map reveals notable challenges and inconveniences. Our research aims to address these issues using remote sensing data and machine learning algorithms (MLAs). We used image processing techniques, including FCCs, PCA, and ICA to identify eight lithological targets and generate reference maps for the study area. The widely used Support Vector Machine (SVM) was trained with informative image combinations, showing reasonable lithological allocations, especially when it fed with FCC 12-6-2 in RGB. Our study found that incorporating dimensionality-reduction techniques like PCA and ICA with false-color images (FCCs) significantly boosted accuracy by over 15%. Using Sentinel-2 imagery and SVM, we created a novel lithological map for the challenging study area, pinpointing mineralization-rich zones, particularly those linked to shear zones within granitic rocks. This map enhances progress in characterizing rock units, and we strongly advocate the use of dimensionality reduction techniques, such as PCA and ICA, to feed machine learning algorithms. These techniques play a crucial role in producing precise, unbiased lithological maps for complex terrains, aiding in the localization of valuable mineral deposits.

Fused remote sensing datasets have greatly contributed to enhancing lithological targets and providing significant information for mineral exploration. For instance, multispectral datasets can discriminate rock units through their unique spectral signatures. Digital Elevation Models (DEMs) could be an effective tool boosting lithological discrimination based mainly on their topographic variations. Consequently, the current study applied the power of the support vector machine (SVM) algorithm and the integrated Phased Array L-type band Synthetic Aperture Radar (PALSAR) DEM and multispectral Sentinel 2 data to: 1—Create an updated lithological map of a poorly mapped arid terrain (Khashir-Ras El-Kharit district, Eastern Desert of Egypt), and 2—Compare and assess the distribution of ferrogabbros with the aim of improving the localization of these rock bodies and investigating their mineral content. Our results enhanced the lithological mapping and revealed a better generalization of mineralized ferrogabbros when the input was a fused DEM with Sentinel 2 compared to the salt and pepper effect observed when adopting the Sentinel 2 solely as the input data. Additionally, the current research strongly recommends detailed exploration programs for Fe-Ti oxide ores within the gabbroic rocks delineated through the resultant thematic map. The ferrogabbros were subjected to a comprehensive analysis involving whole rock geochemistry, XRD, EDX, and light-reflecting investigations, which revealed the existence of magnetite, titanomagnetite, and hematite. The titanomagnetite crystals display subhedral morphology and exhibit fine- to medium-grained surfaces with irregular shapes. X-ray diffraction (XRD) analysis revealed the presence of magnetite and hematite in the concentrated Fe-Ti oxides. Additionally, the whole rock geochemistry highlighted the origin of the ferrogabbros and their proposed tectonic setting as an alkaline to calc-alkaline magma type that developed in a continental arc setting.