Earth Observation Foundation Model PhilEO: Pretraining on the MajorTOM and FastTOM Datasets

Today, Earth Observation (EO) satellites generate massive volumes of data. To fully exploit this, it is essential to pretrain EO Foundation Models (FMs) on large unlabeled datasets, enabling efficient fine-tuning for downstream tasks with minimal labeled data. In this paper, we study scaling-up FMs: we train our models on the pretraining dataset MajorTOM 23TB which includes all regions, and the performance on average is competitive versus models pretrained on more specialized datasets which are substantially smaller and include only land. The additional data of oceans and ice do not decrease the performance on land-focused downstream tasks. These results indicate that large FMs trained on global datasets for a wider variety of downstream tasks can be useful for downstream applications that only require a subset of the information included in their training. The second contribution is the exploration of U-Net Convolutional Neural Network (CNN), Vision Transformers (ViT), and Mamba State-Space Models (SSM) as FMs. U-Net captures local correlations amongst pixels, while ViT and Mamba capture local and distant correlations. We develop various models using different architectures, including U-Net, ViT, and Mamba, and different number of parameters. We evaluate the FLoating-point OPerations (FLOPs) needed by the models. We fine-tune on the PhilEO Bench for different downstream tasks: roads, buildings, and land cover. For most n-shots for roads and buildings, U-Net 200M-2T outperforms the other models. Using Mamba, we achieve comparable results on the downstream tasks, with less computational expenses. We also compare with the recent FM TerraMind which we evaluate on PhilEO Bench.