How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?

Self-supervised pre-training has proven highly effective for many computer
vision tasks, particularly when labelled data are scarce. In the context of
Earth Observation (EO), foundation models and various other Vision Transformer
(ViT)-based approaches have been successfully applied for transfer learning to
downstream tasks. However, it remains unclear under which conditions
pre-trained models offer significant advantages over training from scratch. In
this study, we investigate the effectiveness of pre-training ViT-based Masked
Autoencoders (MAE) for downstream EO tasks, focusing on reconstruction,
segmentation, and classification. We consider two large ViT-based MAE
pre-trained models: a foundation model (Prithvi) and SatMAE. We evaluate
Prithvi on reconstruction and segmentation-based downstream tasks, and for
SatMAE we assess its performance on a classification downstream task. Our
findings suggest that pre-training is particularly beneficial when the
fine-tuning task closely resembles the pre-training task, e.g. reconstruction.
In contrast, for tasks such as segmentation or classification, training from
scratch with specific hyperparameter adjustments proved to be equally or more
effective.