Foundation Models in Earth Science and Remote Sensing

Abstract

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation Models (FMs), AI models designed to replace task-specific models, are increasingly being recognized for their versatility across numerous downstream applications. These models, trained using self-supervised techniques on any type of sequence data, circumvent the need for large annotated datasets, a major bottleneck in traditional AI model development. FMs can be applied to downstream tasks using few-shot learning and fine-tuning, significantly reducing the need for large labeled training datasets and computational resources. In contrast to task-specific models, large-scale FMs facilitate the processing of multi-modal data from different satellites and additional data modalities in order to obtain improved model skills in the Earth observation domain.