Nengxiong Xu

Kaixuan Shao, Gang Mei, Yinghan Wu et al.

Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Available landslide inventories are often sparse and imbalanced, which limits understanding of triggering conditions and failure mechanisms. Data generation provides an effective approach to help capture feature dependencies from limited landslide observations. However, existing generation approaches for landslides often struggle to capture complex relationships among features and lack robustness across multiple scenarios and interacting factors. Here, we propose an accurate and robust approach for generating multi-feature landslide datasets by utilizing a tabular foundation model. By leveraging the capacity to learn from limited observations, the proposed approach effectively preserves the multivariate dependencies and statistical characteristics inherent in landslide occurrences. Comparative experiments on 20 landslide inventories demonstrate that the generated datasets closely align with observed distributions, maintain realistic feature dependencies, and exhibit robustness across different environmental contexts. This work provides an effective approach to overcome data sparsity and imbalance and strengthens landslide susceptibility modeling and risk assessment under limited observations.

Gang Mei, Nengxiong Xu, Liangliang Xu et al.

This paper briefly reports the GeoMFree3D, a meshfree / meshless software package designed for analyzing the problems of large deformations and crack propagations of rock and soil masses in geotechnics. The GeoMFree3D is developed based on the meshfree RPIM, and accelerated by exploiting the parallel computing on multi-core CPU and many-core GPU. The GeoMFree3D is currently being under intensive developments. To demonstrate the correctness and effectiveness of the GeoMFree3D, several simple verification examples are presented in this paper. Moreover, future work on the development of the GeoMFree3D is introduced.

Han Meng, Gang Mei, Hong Tian et al.

Rock discontinuities critically govern the mechanical behavior and stability of rock masses. Their internal distributions remain largely unobservable and are typically inferred from surface-exposed discontinuities using generative prediction approaches. However, surface-exposed observations are inherently sparse, and existing generative prediction approaches either fail to capture the underlying complex distribution patterns or lack robustness under data-sparse conditions. Here, we proposed a simple yet robust approach for statistically accurate generative prediction of rock discontinuities by utilizing a tabular foundation model. By leveraging the powerful sample learning capability of the foundation model specifically designed for small data, our approach can effectively capture the underlying complex distribution patterns within limited measured discontinuities. Comparative experiments on ten datasets with diverse scales and distribution patterns of discontinuities demonstrate superior accuracy and robustness over conventional statistical models and deep generative approaches. This work advances quantitative characterization of rock mass structures, supporting safer and more reliable data-driven geotechnical design.

Meijun Zhou, Gang Mei, Zhengjing Ma et al.

Tracking the spatiotemporal evolution of large-scale landslide scars is critical for understanding the evolution mechanisms and failure precursors, enabling effective early-warning. However, most existing studies have focused on single-phase or pre- and post-failure dual-phase landslide identification. Although these approaches delineate post-failure landslide boundaries, it is challenging to track the spatiotemporal evolution of landslide scars. To address this problem, this study proposes a novel and universal framework for tracking the spatiotemporal evolution of large-scale landslide scars using a vision foundation model. The key idea behind the proposed framework is to reconstruct discrete optical remote sensing images into a continuous video sequence. This transformation enables a vision foundation model, which is developed for video segmentation, to be used for tracking the evolution of landslide scars. The proposed framework operates within a knowledge-guided, auto-propagation, and interactive refinement paradigm to ensure the continuous and accurate identification of landslide scars. The proposed framework was validated through application to two representative cases: the post-failure Baige landslide and the active Sela landslide (2017-2025). Results indicate that the proposed framework enables continuous tracking of landslide scars, capturing both failure precursors critical for early warning and post-failure evolution essential for assessing secondary hazards and long-term stability.

Yuting Yang, Gang Mei, Zhengjing Ma et al.

Small Earth data are geoscience observations with limited short-term monitoring variability, providing sparse but meaningful measurements, typically exhibiting spatiotemporal correlations. Spatiotemporal forecasting on such data is crucial for understanding geoscientific processes despite their small scale. However, conventional deep learning models for spatiotemporal forecasting requires task-specific training for different scenarios. Foundation models do not need task-specific training, but they often exhibit forecasting bias toward the global mean of the pretraining distribution. Here we propose a simple and robust approach for spatiotemporally correlated small Earth data forecasting. The essential idea is to characterize and quantify spatiotemporal patterns of small Earth data and then utilize tabular foundation models for accurate forecasting across different scenarios. Comparative results across three typical scenarios demonstrate that our forecasting approach achieves superior accuracy compared to the graph deep learning model (T-GCN) and tabular foundation model (TabPFN) in the majority of instances, exhibiting stronger robustness.