21 citations · 28 across the 5 of their papers we have counts for
6 papers
Spatially Organized Hillslope Weakening Localizes Retrogressive Thaw Slumps across the Tibetan Plateau
Yuting Yang, Gang Mei, Nengxiong Xu +2
Long-term permafrost warming on the Tibetan Plateau is continuously and broadly reshaping cold-region landscapes, and has produced numerous localized and clustered retrogressive th…
Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
Yuting Yang, Gang Mei, Feng Chen +2
Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require suffic…
Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
Kaixuan Shao, Gang Mei, Yinghan Wu +2
Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Available landslide inventories…
Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting
Zhengjing Ma, Gang Mei
Forecasting how landslides will evolve over time or whether they will fail is a challenging task due to a variety of factors, both internal and external. Despite their considerable…
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving
Jingzhi Tu, Gang Mei, Francesco Piccialli
Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing g…
Deep Transfer Learning for Identifications of Slope Surface Cracks
Yuting Yang, Gang Mei
Geohazards such as landslides have caused great losses to the safety of people's lives and property, which is often accompanied with surface cracks. If such surface cracks could be…