Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model

TL;DR

This paper introduces a robust generative method using a tabular foundation model to create realistic, balanced landslide datasets from limited observations, enhancing landslide modeling and risk assessment.

Contribution

The proposed approach effectively captures complex feature dependencies and maintains statistical characteristics, improving data generation for landslide analysis under data scarcity.

Findings

Generated datasets closely match observed distributions.

Method demonstrates robustness across diverse environmental scenarios.

Preserves multivariate dependencies in landslide data.

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.