Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analyzes the spatiotemporal trends of 1352 non-glacial lakes (>1 km²) on the TP from 2014 to 2023 using high-resolution Gaofen-1 (GF-1) and Gaofen-2 (GF-2) imagery. By integrating geomorphic analysis with hazard mechanisms, we screened and categorized lakes prone to outburst floods and inundation using a classification and assessment framework proposed in this study. The results indicate that the net area of these lakes expanded by 2839.53 km² (6.07%), with the Inner TP Basin contributing the largest absolute area gain (1960.60 km²). We identified 21 potentially hazardous lakes (10 outburst-prone and 11 inundation-prone) and systematically categorized them by risk level. Field investigations of high-risk candidates, such as Rulei Co and Xiao Qaidam Lake, validated the accuracy of the hazard classification and risk assessment methodology. Preliminary attribution analysis further suggests that the two hazard types may be associated with distinct climatic factors. Overall, this study provides a scientific basis for disaster mitigation and lake management on the TP.

Accurate lake boundary extraction from optical remote sensing imagery remains challenging in high-altitude regions such as the Tibetan Plateau due to ice cover, snow, shadows, and spectrally similar backgrounds. Although recent deep learning models achieve strong region-overlap performance, they often fail to ensure stable shoreline localization. To address this issue, we propose a Global–Local Residual Refinement Network (GLR-Net) for boundary-aware lake extraction from remote sensing imagery. The proposed framework first captures large-scale semantic context through a global branch and subsequently performs patch-level residual refinement to improve local shoreline geometry. A global-to-local guidance mechanism is further introduced to incorporate structural priors into local refinement. Experiments on a manually annotated Tibetan Plateau lake dataset demonstrate that the proposed method achieves competitive region-level segmentation performance while improving geometric shoreline accuracy. Compared with representative semantic segmentation baselines, including U-Net, SegFormer-B0, SegFormer-B4, and OCRNet, the proposed method achieves the highest Boundary F1 score of 0.811 under a 3-pixel tolerance and the lowest mean BDE of 13.19 pixels. The results indicate that conventional overlap-based metrics alone are insufficient for evaluating shoreline delineation quality in complex alpine environments.