The Sentinel-2 (S2) satellite constellation offers new opportunities for monitoring the Earth. However, the raw data is difficult to use due to cloud cover and other limitations (10m resolution limited for analysis at fine scale, 16-bits representation that prevents from large scale visualization for example). As a consequence, several platforms have emerged to synthesize the data and offer easy-to-use products over the past years.

Among them, NIMBO is a free, cloud-based platform that provides users with access to monthly images of the entire Earth. The platform uses deep neural networks to remove clouds from satellite images, colorizing images and providing users with a clear view of the Earth's surface.

Providing homogeneous images makes it easier to develop large-scale tasks, in particular by creating reliable and robust datasets for various applications. This is illustrated in this paper, through two important applications: change detection and super-resolution.