The article explores the potential of remote sensing (RS) applications for identifying patterns of rare metal mineralization in the studied area of Eastern Kazakhstan. An analysis of the technical parameters related to RS data is conducted to highlight the structural and lithological features, which allows for a deeper understanding of the geological environment and the region's potential resources. The research is based on multispectral satellite data obtained within potentially promising magmatogenic ore systems. The authors emphasize the identification of areas with a high probability of discovering rare metal mineralization, which can play a crucial role in the exploration and development of deposits. A key element of the study is the analysis of spectral characteristics of rare metal pegmatites and their constituent minerals, enabling the establishment of their unique signatures in the spectral data. The article also discusses the methods for processing and interpreting multispectral images, which are essential for accurately identifying minerals and assessing their distribution. Modern machine learning techniques are applied in the research, significantly increasing the efficiency of data analysis and allowing for the automation of mineral identification and classification processes. As a result, meaningful conclusions are drawn about the region's potential for rare metal extraction, opening new prospects for further research and practical applications in geological exploration.

This paper studies the influence of meteorological factors on avalanche occurrence in East Kazakhstan using modern data analysis methods. A dataset of 111 avalanche events in nine avalanche-prone areas of the region, recorded between 2012 and 2023, was compiled. Primary data on avalanche dates were obtained from the Department of Emergency Situations of East Kazakhstan Region (DES EKR), and meteorological data were sourced from the Kazhydromet website. Descriptive statistics, correlation analysis, principal component analysis (PCA), as well as K-means clustering and DBSCAN algorithms, were used for the analysis. During the analysis of meteorological conditions preceding avalanches at nine avalanche-prone areas in Eastern Kazakhstan, using PCA (Principal Component Analysis), the main weather factors affecting avalanche formation were determined. Clustering of 111 avalanches using the K-Means method allowed the identification of four scenario types: gradual snow accumulation without wind (33 cases), upper layer thawing due to warming (34), high snow cover (28), and storm impact (16). The DBSCAN method revealed two anomalous cases related to extreme snow depth. Correlation analysis revealed significant relationships between avalanches and meteorological parameters such as air temperature, snow cover depth, wind speed and direction, precipitation, and relative humidity. Correlation analysis revealed both negative and positive relationships between meteorological parameters. Principal component analysis identified the most significant variables affecting avalanche activity, with temperature, snow cover height, and wind making the greatest contributions. Cluster analysis demonstrated that avalanches could occur under different combinations of weather conditions within the same areas, confirming the complex nature of avalanche-forming processes. The results emphasize the need for an integrated approach to avalanche forecasting that accounts for the multi-parametric interactions of meteorological factors, and may contribute to the improvement of avalanche risk monitoring and mitigation systems in mountain regions.

The article addresses the pressing issue of water security, which has emerged as a significant global risk in the 21st century. With the annual increase in water resource demands due to population growth and socio-economic factors, Kazakhstan faces unique challenges related to its geographical position and reliance on transboundary rivers. Eastern Kazakhstan is one of the most water-rich regions in the country, featuring the Irtysh River and numerous tributaries. Meltwater annually causes flooding, resulting in significant damage, while its potential remains underutilized in agriculture. n this study, remote sensing technologies were applied through the processing of satellite imagery from NOAA, TERRA, AQUA, Sentinel-1, and Sentinel-2 for the period 2017–2022 to analyze flood events. A digital terrain model (DTM) was constructed to delineate watershed areas. The results indicate that floods are primarily associated with spring snowmelt and predominantly affect regions such as Abai, Ayagoz, and Zaysan, with a potential inundation area of up to 468,9 km². To address these issues, the article proposes the creation of reserve reservoirs to manage surface runoff and drainage from temporarily flooded areas. Research has identified 12 potential sites for the creation of reservoirs, which will not only minimise flood damage but also make effective use of meltwater. This strategy not only reduces flood risks but also improves the availability of water resources for agriculture during dry periods. The results show that the use of modern technologies can significantly improve water resource forecasting and management, which in turn contributes to food security and sustainable natural resource use in a changing climate.

Industrial facilities regularly require reconstruction or replacement of individual elements. To solve such problems, design organisations are increasingly using digital 3D models, which are created using data from terrestrial laser scanning, among other sources. s an example, several facilities of “Kazzinc” LLP were examined, covering the entire workflow from field surveys to the final results. During laser scanning, the average error in point cloud alignment was no more than 7 mm, which meets the requirements for data accuracy between stations of no more than 15 mm.The article highlights the advantages of terrestrial laser scanning (TLS), such as high measurement accuracy and reduced human error. The analysis of deviations from design specifications revealed potential conflicts, which helps to prevent delays and additional costs.Despite these achievements, the main limiting factor remains the lack of regulatory and technical documentation in Kazakhstan governing the use of TLS. In conclusion, it is emphasized that the further development and standardization of TLS methods could significantly enhance the quality and safety of engineering works.

Rare metal exploration increasingly relies on the integration of heterogeneous geological datasets and advanced analytical methods to improve the efficiency and reliability of mineral prospecting. This study presents the development of a web-based Geographic Information System, Geospatial Information System for Optimized Rare Metal Exploration in Eastern Kazakhstan (GISORMEK), using the central part of the Kalba–Narym rare-metal belt (Eastern Kazakhstan) as a case study. A comprehensive geospatial database was developed through the digitization of archival geological maps and the integration of geological, geochemical, tectonic, geophysical, geomorphological, and mineral occurrence datasets. To complement historical mapping data, Landsat-8 multispectral imagery was incorporated to improve lithological discrimination and identify hydrothermal alteration zones. Two remote sensing techniques were applied: Principal Component Analysis (PCA) for lithological mapping and enhancement of geological features, and band ratio (BR) analysis for the calculation of geological spectral indices, including the iron oxide index and the hydroxyl-bearing (Al–OH) mineral index. The resulting spectral indices were subsequently integrated to generate a predictive hydrothermal alteration map. GISORMEK integrates historical and contemporary datasets within a unified web-GIS framework, ensuring spatial consistency, reproducibility, and accessibility. The proposed framework enhances the interpretation of the mineralization potential of the Kalba–Narym region and provides geospatial platform for supporting rare metal exploration and future mineral prospectivity assessments.

A rapid and spatially explicit assessment of structural damage is essential following major disasters, particularly where access to the field is limited and reliable information on damage is scarce. This study evaluates how semantic segmentation architecture, temporal input design, and class formulation influence the detection of collapsed buildings in very high-resolution Pléiades imagery from Jindires, Syria, following the earthquakes of 6 February 2023. The U-Net, SegNet, DeepLabv3+, and fully convolutional network (FCN) models were compared together with a reference random forest model. Methodologically, this study employs a controlled factorial experiment designed to isolate the effects of using either post-event images alone or a combination of pre- and post-event imagery, across both class-based and binary label formulations. Performance was evaluated in a spatially separate validation region using Dice loss, overall accuracy, and pixel-level F1-score, supplemented by location-level evaluation for 102 visually interpreted sites of collapsed buildings. The FCN with multitemporal binary input achieved the highest F1-score at the pixel level (79.1%), followed by the U-Net network with multitemporal three-class input (76.0%) and post-event binary input (74.8%). At the site level, both the U-Net network with post-event binary input and the FCN with multitemporal binary input achieved an F1-score of 73.0%; the U-Net network prioritized recall, while the FCN prioritized precision. The results reveal that the optimal configuration depends heavily on the chosen architecture’s inductive biases. Therefore, these findings highlight the importance of selecting not only the right architecture, but also the appropriate temporal input and class formulation. The results provide reproducible information for screening; although they do not constitute a definitive inventory of damage, they offer a truly independent perspective on the consequences of the earthquake in a region from which verified data cannot be obtained.