PulseAugur
EN
LIVE 23:43:26

Tabular foundation models improve landslide prediction with scarce data

By PulseAugur Editorial · [4 sources] ·

Two new research papers propose novel approaches to landslide susceptibility prediction using tabular foundation models, addressing the common issue of data scarcity and imbalance. The first paper introduces a generative method to create realistic landslide datasets, preserving complex feature dependencies and demonstrating robustness across various scenarios. The second paper presents a knowledge-data dual-driven paradigm that integrates geomorphic prior knowledge with limited landslide data, achieving comparable accuracy to traditional methods that require significantly more data. AI

IMPACT These methods could significantly improve geological hazard assessment in data-scarce regions by enhancing the accuracy of landslide susceptibility models.

RANK_REASON Two academic papers published on arXiv proposing new methodologies for landslide prediction using tabular foundation models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Tabular foundation models improve landslide prediction with scarce data

COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Kaixuan Shao, Gang Mei, Yinghan Wu, Nengxiong Xu, Jianbing Peng ·

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

    arXiv:2604.25159v1 Announce Type: new 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 under…

  2. arXiv cs.LG TIER_1 English(EN) · Yuting Yang, Gang Mei, Feng Chen, Yongshuang Zhang, Jianbing Peng ·

    Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

    arXiv:2604.25196v1 Announce Type: new Abstract: Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require sufficient conditioning factors and large-scale landslide …

  3. arXiv cs.LG TIER_1 English(EN) · Jianbing Peng ·

    Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

    Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require sufficient conditioning factors and large-scale landslide inventories. However, in practical engineering a…

  4. arXiv cs.LG TIER_1 English(EN) · Jianbing Peng ·

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

    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 me…

RELATED ENTITIES

RELATED TOPICS