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.

Oil spills pose significant threats to marine and coastal ecosystems, necessitating advanced methodologies for environmental sensitivity and vulnerability assessments. This study enhances existing frameworks to better manage oil spill risks in the Caspian Sea, a region characterized by its ecological sensitivity and economic dependence on oil extraction. Utilizing the Environmental Sensitivity Index (ESI), we adapted global standards to the unique conditions of the Caspian Sea and built a sensitivity map of the coastline, which later became one of the components of the integral sensitivity map for the entire Caspian Sea, which includes several biotic and abiotic components. We also developed a comprehensive geodatabase incorporating topographic, infrastructural, and hydrodynamic data. Through the sophisticated modeling of oil spill scenarios using the Oil Spill model of the MIKE 21 software (Release 2016) suite, we simulated spills of varying magnitudes to analyze their potential impacts on the marine and coastal environment. The results enabled the creation of vulnerability maps, pinpointing areas at highest risk and facilitating strategic response planning. Our study demonstrates the critical importance of integrating advanced geospatial analyses and dynamic modeling techniques to improve oil spill preparedness and response strategies. The findings of this study suggest that enhanced monitoring and adaptive management strategies are essential for protecting the Caspian Sea from environmental risks posed by its oil industry.

While rock glaciers (RGs) are widespread in the Zhetysu Alatau mountain range of Tien Shan (Kazakhstan), they have not yet been systematically investigated. In this study, we present the first rock glacier inventory of this region containing 256 rock glaciers with quantitative information about their locations, geomorphic parameters, and downslope velocities, as established using a method that combines SAR interferometry and optical images from Google Earth. Our inventory shows that most of the RGs are talus-derived (61%). The maximum downslope velocity of the active rock glaciers (ARGs) was 252 mm yr⁻¹. The average lower height of rock glaciers in this part of the Zhetysu Alatau was 3036 m above sea level (ASL). The largest area of rock glaciers was located between 2800 and 3400 m ASL and covered almost 86% of the total area. Most rock glaciers had a northern (northern, northeastern, and northwestern) orientation, which indicated the important role of solar insolation in their formation and preservation.

Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to high-mountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional field-based limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions.

An updated glacier inventory is important for understanding the current glacier dynamics in the conditions of actual accelerating glacier retreat observed around the world. Here, we present a detailed analysis of the glaciation areas of the Zhetysu Alatau Range (Tien Shan) for 1956–2016 using well-established semiautomatic methods based on the band ratios. The total glacier area decreased by 49 ± 2.8% or by 399 ± 11.2 km² from 813.6 ± 22.8 km² to 414.6 ± 11.6 km² during 1956–2016, while the number of glaciers increased from 985 to 813. Similar rates of area change characterized the periods 1956–2001, 2001–2012, 2012–2016, and 2001–2016: −296.2 ± 8.3 (−0.8% a⁻¹), −63.7± 1.8 (−1.1% a⁻¹), −39.1 ±1.1 (−2.2% a⁻¹) and −102.8 ± 2.9 (−1.3% a⁻¹) km², respectively. The mean glacier size decreased from 0.57 km² in 2001 to 0.51 km² in 2016. Most glaciation areas of the Zhetysu Alatau faced north (north, northwest, and northeast), covered 390.35 ± 11 km², and were located in altitudes between 3000 and 4000 m.a.s.l. With shrinkage rates of about −0.8% and −1.3% a⁻¹ for the periods of 1956–2001 and 2001–2016, our results show that study area has the highest shrinkage rate compared to other glacierized areas of Central Asian mountains, including Altai, Pamir, and even the inner ranges of Tien Shan. It was found that a significant increase in temperature (0.12 °C/10 years) plays a main role in the state of glaciers.

Assessing glaciers using recent and historical data and predicting the future impacts on them due to climate change are crucial for understanding global glacier mass balance, regional water resources, and downstream hydrology. Computational methods are crucial for analyzing current conditions and forecasting glacier changes using remote sensing and other data sources. Due to the complexity and large data volumes, there is a strong demand for accelerated computing. AI-based approaches are increasingly being adopted for their efficiency and accuracy in these tasks. Thus, in the current state-of-the-art review work, available research results on the application of AI methods for glacier studies are addressed. Using selected search terms, AI-based publications are collected from research databases. They are further classified in terms of their geographical locations and glacier-related research purposes. It was found that the majority of AI-based glacier studies focused on inventorying and mapping glaciers worldwide. AI techniques like U-Net, Random forest, CNN, and DeepLab are mostly utilized in glacier mapping, demonstrating their adaptability and scalability. Other AI-based glacier studies such as glacier evolution, snow/ice differentiation, and ice dynamic modeling are reviewed and classified, Overall, AI methods are predominantly based on supervised learning and deep learning approaches, and these methods have been used almost evenly in glacier publications over the years since the beginning of this research area. Thus, the integration of AI in glacier research is advancing, promising to enhance our comprehension of glaciers amid climate change and aiding environmental conservation and resource management.