Sarez Lake, situated at one of the highest altitudes among naturally dammed lakes, is regarded as potentially hazardous due to its geological setting. Therefore, developing an integrated monitoring and risk assessment framework for slope-related geological hazards in this region holds significant scientific and practical value. In this study, we processed 220 Sentinel-1A SAR images acquired between 12 March 2017 and 2 August 2024, using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to extract time-series deformation data with millimeter-level precision. These deformation measurements were combined with key environmental factors to construct a susceptibility evaluation model based on the Information Value and Support Vector Machine (IV-SVM) methods. The results revealed a distinct spatial deformation pattern, characterized by greater activity in the western region than in the east. The maximum deformation rate along the shoreline increased from 280 mm/yr to 480 mm/yr, with a marked acceleration observed between 2022 and 2023. Geohazard susceptibility in the Sarez Lake area exhibits a stepped gradient: the proportion of area classified as extremely high susceptibility is 15.26%, decreasing to 29.05% for extremely low susceptibility; meanwhile, the density of recorded hazard sites declines from 0.1798 to 0.0050 events per km². The spatial configuration is characterized by high susceptibility on both flanks, a central low, and convergence of hazardous zones at the front and distal ends with a central expansion. These findings suggest that mitigation efforts should prioritize the detailed monitoring and remediation of steep lakeside slopes and fault-associated fracture zones. This study provides a robust scientific and technical foundation for the emergency warning and disaster management of high-altitude barrier lakes, which is applicable even in data-limited contexts.

The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric Synthetic Aperture Radar (InSAR) data. By employing an advanced stacked LSTM network model, we effectively capture temporal dependencies and move beyond traditional methods that depend on explicit deformation. This approach enables short- to medium-term deformation prediction through structured time dynamic modeling, identifies potential landslide targets in the high-altitude regions upstream of Lake Sarez, and classifies associated risk levels. The results indicate that: (1) In short-term forecasting, the stacked LSTM model effectively captures trend turning points, producing stable and reliable predictions with a Mean Absolute Error (MAE) of 0.164 mm and a Root Mean Square Error (RMSE) of 0.194 mm; (2) From 2019 to 2022, regional surface deformation characteristics exhibited significant spatial heterogeneity, with the potential landslide on the right bank identified as the most critical settlement center, demonstrating a line of sight (LOS) deformation rate consistently exceeding 49 mm per year, while the Usoi Dam displayed relatively good stability during this period; (3) By integrating InSAR deformation rate maps with Sentinel-2 optical images, we identified a total of 72 potential landslide targets in the region, four of which exhibited deformation rates exceeding −30 mm per year, indicating significant activity and classifying them as high-risk areas requiring attention. This provides a targeted reference list for the prevention and control of geological landslides around Lake Sarez and establishes a reliable technical pathway for the early identification of landslides under complex geological conditions in high-altitude mountainous areas.

The collaborative use of camera near-field sensors for monitoring the number and status of tourists is a crucial aspect of smart scenic spot management. This paper proposes a near-field perception technical system that achieves dynamic and accurate detection of tourist targets in mountainous scenic areas, addressing the challenges of real-time passive perception and safety management of tourists. The technical framework involves the following steps: Firstly, real-time video stream signals are collected from multiple cameras to create a distributed perception network. Then, the YOLOX network model is enhanced with the CBAM module and ASFF method to improve the dynamic recognition of preliminary tourist targets in complex scenes. Additionally, the BYTE target dynamic tracking algorithm is employed to address the issue of target occlusion in mountainous scenic areas, thereby enhancing the accuracy of model detection. Finally, the video target monocular spatial positioning algorithm is utilized to determine the actual geographic location of tourists based on the image coordinates. The algorithm was deployed in the Tianmeng Scenic Area of Yimeng Mountain in Shandong Province, and the results demonstrate that this technical system effectively assists in accurately perceiving and spatially positioning tourists in mountainous scenic spots. The system demonstrates an overall accuracy in tourist perception of over 90%, with spatial positioning errors less than 1.0 m and a root mean square error (RMSE) of less than 1.14. This provides auxiliary technical support and effective data support for passive real-time dynamic precise perception and safety management of regional tourist targets in mountainous scenic areas with no/weak satellite navigation signals.

Exploring the spatiotemporal patterns of cultivated land conversion to non-agricultural uses and their evolutionary driving mechanisms is of significant importance for regional cultivated land protection and food security. This study utilizes time-series land use datasets, DEM, meteorological data, and statistical yearbook data to construct an assessment model for the rate of cultivated land conversion to non-agricultural uses. Based on this model, the study conducts spatial autocorrelation analysis and locational gradient analysis to systematically investigate the characteristics and driving mechanisms of cultivated land conversion to non-agricultural uses in Jiangsu Province from 2000 to 2023. The study revealed several key findings: (1). The total area of cultivated land in Jiangsu Province has demonstrated a trend of ‘initial continuous decline followed by a slight recovery after 2015.’ Spatially, it exhibits a distribution pattern characterized by ‘continuous reduction around urban areas, with relative stability in the northern core regions’. (2). The temporal pattern of cultivated land conversion to non-agricultural use in Jiangsu Province follows a trajectory of ‘rapid expansion (2000–2015) followed by a gradual slowdown (2015–2023),’ with significant gradient differences observed spatially (‘Southern Jiangsu > Central Jiangsu > Northern Jiangsu’). (3). The conversion of cultivated land to non-agricultural use in Jiangsu Province results from the combined effects of natural constraints, socio-economic driving factors, and agricultural policies. Topographical constraints and urban radiation have emerged as the primary spatial conditions promoting non-agriculturalization, with urban expansion identified as the most direct driving factor of cultivated land conversion in recent years. Conversely, agricultural factors have exerted a relatively weaker influence on non-agriculturalization. These research findings provide a significant scientific basis for formulating differentiated cultivated land protection policies across the province, thereby assisting in achieving a balance between food security and coordinated urban–rural development.

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments.