Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Water, as an indispensable constituent of life, serves as the primary source of sustenance for all living things on Earth. The contamination of surface water with heavy metals poses a significant global health risk to humans, animals, and plants. Sharkiya Governorate, situated in the East Nile Delta region of Egypt, is particularly susceptible to surface water pollution due to various industrial, agricultural, and urban activities. The Bahr Mouse Stream, crucial for providing potable water and supporting irrigation activities in Sharkiya Governorate, caters to a population of approximately 7.7 million inhabitants. Unfortunately, this vital water source is exposed to many illegal encroachments that may cause pollution and deteriorate the water resource quality. In a comprehensive study conducted over two consecutive seasons (2019–2020), a total of 38 surface water samples were taken to assess the quantity of heavy metals in surface water destined for human consumption and other applications, supported by indices and statistics. The assessment utilized flame atomic absorption spectrophotometry to determine the concentration of key heavy metals including iron (Fe), manganese (Mn), cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), cobalt (Co), and chromium (Cr). The calculated mean value of the Water Quality Index (WQI) was found to be 39.1 during the winter season and 28.05 during the summer season. This value suggests that the surface water maintains good quality and is suitable for drinking purposes. Furthermore, the analysis indicated that the concentrations of heavy metals in the study area were below the recommended limits set by the World Health Organization and fell within the safe threshold prescribed by Egyptian legislation. Despite the identification of localized instances of illegal activities in certain areas, such as unauthorized discharges, the findings affirm that the Bahr Mouse stream is devoid of heavy metal pollution. This underscores the importance of continued vigilance and regulatory enforcement to preserve the integrity of these vital water resources.

The Neoproterozoic banded iron formations (BIFs) are widely occurred in the Egyptian Eastern Desert. This study integrates field observations, petrographic studies, geochemical data, and lead isotopes to construct the genesis and depositional environment of Wadi El-Mis hama BIF deposits. The iron layers, primarily of oxide facies within a volcano-sedimentary sequence, comprise magnetite-rich beds alternating with jaspilite or silicate laminae. The studied BIFs exhibit a dominant composition of SiO₂ and Fe₂O₃^t with relatively low contents of TiO₂ and Al₂O₃. The positive correlation of REEs (La, Sm, Yb) with Zr and low concentrations of HFSEs (Ta, Nb, Th, Hf) indicate a primary formation mechanism of chemical precipitation, maintaining original geochemical signatures. Geochemical patterns show depletion in LREEs, enrichment in HREEs (La/Yb_PAAS = 0.08–0.12), and positive La anomalies (La/La_PAAS = 1.15–8.57), consistent with seawater influence. Additionally, various geochemical discrimination diagrams supported by elevated super-chondritic Y/Ho values (29.6–38.7), weak positive Eu anomalies, and low contents of transition metals (Cu and Zn), point to the interaction of low-temperature (<200°C) hydrothermal fluids (bearing Fe and Si) with seawater during the deposition of the BIFs. The lack of significant negative Ce anomalies along with low Ni/Co, U/Th, and Cu/Zn ratios, imply that the iron mineralization was precipitated from dysoxic to oxic conditions. The geochemical and Pb isotopic data suggest that the iron deposits formed in an extensional geodynamic setting (intra-oceanic arc basin environment) due to the subduction of the Mozambique Plate, with signatures closely matching other Precambrian Algoma-type BIFs.