Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing.Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited.To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models.We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

This is a fast-changing and critical time for Earth Observation (EO), especially for those involved in its use for climate and meteorology. On this course, you'll get a comprehensive overview of the Copernicus Programme and the wealth of EO data it provides, as well as how AI and ML are transforming the interpretation of EO data. You'll learn about the Copernicus data and services and the massive amounts of Earth observation data that are collected every day from space, covering the oceans, land, atmosphere and, over longer periods, the climate. You'll then learn basic AI and ML concepts and types, exploring how they have transformed many aspects of the EO'value chain'. This includes automatic feature extraction, new ways of processing very large data sets, and the development of new products and services.