  Recent progress in self-supervision shows that pre-training large neural
networks on vast amounts of unsupervised data can lead to impressive increases
in generalisation for downstream tasks. Such models, recently coined as
foundation models, have been transformational to the field of natural language
processing. While similar models have also been trained on large corpuses of
images, they are not well suited for remote sensing data. To stimulate the
development of foundation models for Earth monitoring, we propose to develop a
new benchmark comprised of a variety of downstream tasks related to climate
change. We believe that this can lead to substantial improvements in many
existing applications and facilitate the development of new applications. This
proposal is also a call for collaboration with the aim of developing a better
evaluation process to mitigate potential downsides of foundation models for
Earth monitoring.
