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.
翻译:最近自我监督方面的进展表明,就大量不受监督的数据对大型神经网络进行预先培训可导致下游任务总体化的显著提高,最近作为基础模型创建的这类模型已经向自然语言处理领域转变。虽然类似的模型也接受了大量图像的培训,但并不完全适合遥感数据。为刺激开发地球监测基础模型,我们提议制定一个新的基准,其中包括与气候变化有关的各种下游任务。我们认为,这可以大大改进许多现有应用,促进新应用的开发。这个建议也呼吁开展合作,以发展更好的评估进程,减少地球监测基础模型的潜在下坡面。