Self-supervised learning provides a powerful means to pretrain models based on un-labeled data. Un-labeled Earth observation data are abundant: this circumstance combined with the availability of multi modal data makes Earth observation a perfect playground for self-supervised learning. Our early results are very promising…

We developed a deep learning model that is able to distinguish between 10 different types of power plants in an effort to automatically identify and characterize industrial sites in satellite imagery. This work will help us to estimate greenhouse gas emission rates for individual industrial sites in the future.