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Generating realistic landslide datasets from sparse, imbalanced real-world data is now possible, thanks to a tabular foundation model that captures complex feature dependencies.
Accurate landslide prediction is possible with sparse data by injecting geomorphic priors, unlocking geohazard risk assessment in data-scarce mountainous regions.
