In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas.

Mineral resources, particularly copper, are crucial for the sustained economic growth of developing countries like Kazakhstan. Over the past four decades, the diversity and importance of critical minerals for high technology and environmental applications have increased dramatically. Today, copper is a critical metal due to its importance in electrification. Porphyry deposits are important sources of copper and other critical metals. Conventional exploration methods for mapping alteration zones as indicators of high-potential zones in porphyry deposits are often associated with increased cost, time and environmental concerns. Remote sensing imagery is a cutting-edge technology for the exploration of minerals at low cost and in short timeframes and without environmental damage. Kazakhstan hosts several large porphyry copper deposits, such as Aktogay, Aidarly, Bozshakol and Koksai, and has great potential for the discovery of new resources. However, the potential of these porphyry deposits has not yet been fully discovered using remote sensing technology. In this study, a remote sensing-based mineral exploration approach was developed to delineate hydrothermal alteration zones associated with Aktogay porphyry copper mineralization in eastern Kazakhstan using Landsat-8 and ASTER satellite sensors. A comprehensive suite of image processing techniques was used to analyze the two remote sensing datasets, including specialized band ratios (BRs), principal component analysis (PCA) and the Crosta method. The remote sensing results were validated against field data, including the spatial distribution of geological lineaments and petrographic analysis of the collected rock samples of alteration zones and ore mineralization. The results show that the ASTER data, especially when analyzed with specialized BRs and the Crosta method, effectively identified the main hydrothermal alteration zones, including potassic, propylitic, argillic and iron oxide zones, as indicators of potential zones of ore mineralization. The spatial orientation of these alteration zones with high lineament density supports their association with underlying mineralized zones and the spatial location of high-potential zones. This study highlights the high applicability of the remote sensing-based mineral exploration approach compared to traditional techniques and provides a rapid, cost-effective tool for early-stage exploration of porphyry copper systems in Kazakhstan. The results provide a solid framework for future detailed geological, geochemical and geophysical studies aimed at resource development of the Aktogay porphyry copper mineralization in eastern Kazakhstan. The results of this study underpin the effectiveness of remote sensing data for mineral exploration in geologically complex regions where limited geological information is available and provide a scalable approach for other developing countries worldwide.

Effective and sustainable resource management is critical to the mining industry, which plays a significant role in the Kazakhstan economy. To achieve this goal, the use of remote sensing technologies has become necessary. These advanced tools are now integral to activities such as mineral mapping, research and environmental monitoring. Therefore, the purpose of this comprehensive review article is to scrutinize how remote sensing data is used specifically in the context of mining in Kazakhstan. The comprehensive review covers a wide range of aspects, including the use of remote sensing for mapping and mineral resource studies. Key findings highlight the growing importance of remote sensing technologies in Kazakhstan's mining industry as they facilitate the precise delineation of ore deposits, enable effective monitoring of environmental impacts and promote optimal use of natural resources. The article discusses the difficulties and limitations encountered in the application of remote sensing data. Particular attention is paid to the need to improve methods of data processing, correction of atmospheric influences and spectral analysis. The overview presented in this paper is of great importance to those involved in research, policy development and industrial sectors who are interested in using remote sensing data to enhance mining activities and promote environmental sustainability in Kazakhstan. By providing a comprehensive analysis of the subject, this review offers valuable information that can help researchers deepen their understanding of the topic and guide them toward more effective strategies.

The Koldar Massif in southeastern Kazakhstan is a geologically complex area with potential for porphyry copper and rare-metal mineralization. This study applies a multi-scale remote sensing approach to delineate hydrothermal alteration zones using medium-resolution ASTER imagery and very high-resolution WorldView-3 data. Image processing techniques—including false color composites (FCCs), band ratios (BRs), and the Spectral Angle Mapper (SAM)—were employed across the VNIR and SWIR bands to detect alteration minerals such as kaolinite, illite, montmorillonite, chlorite, epidote, calcite, quartz, and muscovite. These minerals correspond to argillic, propylitic, and phyllic alteration zones. While ASTER supported regional-scale mapping, WorldView-3 enabled detailed analysis at the Aidarly deposit. Validation was performed using copper occurrences, lithogeochemical anomaly contours, and ore body boundaries. The results show a strong spatial correlation between the mapped alteration zones and known mineralization patterns. Importantly, this study reports the identification of a previously undocumented hydrothermal zone north of the Aidarly deposit, detected using WorldView-3 data. This zone exhibits concentric phyllic and argillic alterations, similar to those at Aidarly, and may represent an extension of the mineralized system. Unlike earlier studies on the Aktogay deposit based on ASTER and Landsat-8, this work focuses on the Aidarly deposit and introduces higher-resolution analysis and SAM-based classification, offering improved spatial accuracy and target delineation. The proposed methodology provides a reproducible and scalable workflow for early-stage mineral exploration in underexplored regions, especially where field access is limited. These results highlight the value of high-resolution remote sensing in detecting concealed porphyry copper systems in structurally complex terrains.