Uncertainty in deep learning models with application to remote sensing

Building upon state of the art deep learning architectures for earth observation data, we will develop an optimized learning pipeline for hyperspectral imaging. Equivariant data augmentation strategies such as elastic deformations of the input imagery and associated labels will be employed to mitigate issues related to sparse data annotations. We will augment this learning architecture with robust uncertainty estimates.