2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
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…
cs.LG2026
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…
cs.LG2023★ 2 cited
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…