Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.25159 [cs.LG] |
| (or arXiv:2604.25159v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25159 arXiv-issued DOI via DataCite (pending registration) |
From: Gang Mei [view email]
[v1]
Tue, 28 Apr 2026 02:59:58 UTC (9,501 KB)
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