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Artifact removal is an integral component of cinematic scientificvisualization, and is especially challenging with big datasets in whichartifacts are difficult to define. In this paper, we describe a method forcreating cloud artifact masks which can be used to remove artifacts fromsatellite imagery using a combination of traditional image processing togetherwith deep learning based on U-Net. Compared to previous methods, our approachdoes not require multi-channel spectral imagery but performs successfully onsingle-channel Digital Elevation Models (DEMs). DEMs are a representation ofthe topography of the Earth and have a variety applications including planetaryscience, geology, flood modeling, and city planning.

CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data