High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects.
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X).
A complete map of our daily waters can give us an early warning for where droughts are born: the receding tips of the flowing network.
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding.