Balanced, globally representative pre-training data generally outperforms region-specific sampling for two geospatial foundation models in few-shot downstream tasks, and the advantage shrinks as finetuning data grows.
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How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
Balanced, globally representative pre-training data generally outperforms region-specific sampling for two geospatial foundation models in few-shot downstream tasks, and the advantage shrinks as finetuning data grows.