How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
This work addresses the problem of optimizing pre-training data selection for GFMs, which is incremental as it builds on prior work focused on model architecture and pre-text tasks.
The study investigated how the geographic distribution of pre-training data affects the performance of Geospatial Foundation Models (GFMs), finding that balanced and globally representative data compositions often outperform region-specific sampling in downstream tasks.
Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate change, agriculture, and disaster response. Previous work on GFMs focused on tailoring model architecture and pre-text tasks, and did not investigate the impact of pre-training data selection on model performance. However, recent works from other domains show that the pre-training data distribution is an important factor influencing the performance of the foundation models. With this motivation, our research explores how the geographic distribution of pre-training data affects the performance of GFMs. We evaluated several pre-training data distributions by sampling different compositions from a global data pool. Our experiments with two GFMs on downstream tasks indicate that balanced and globally representative data compositions often outperform region-specific sampling, highlighting the importance of diversity and global coverage in pre-training data. Our results suggest that the most appropriate data sampling technique may depend on the specific GFM architecture. These findings will support the development of robust GFMs by incorporating quality pre-training data distributions, ultimately improving machine learning solutions for Earth observation.