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

Kalina Borkiewicz, Viraj Shah, Jill P. Naiman, Chuanyue Shen, Stuart Levy, Jeff Carpenter

Abstract: Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.