Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark

[brunosan]

https://arxiv.org/pdf/2112.00570.pdf

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.