@inproceedings{666068f1486047719564d66aa74f6761,
title = "CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data",
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.",
keywords = "broad impact, cinematic scientific visualization, data preparation, data processing, data visualization, deep learning, image processing, machine learning, public outreach, science communication, u net",
author = "Kalina Borkiewicz and Viraj Shah and Naiman, {J. P.} and Chuanyue Shen and Stuart Levy and Jeff Carpenter",
note = "Thank you to Donna Cox, Bob Patterson, AJ Christensen, Saurabh Gupta, Sebastian Frith, and the reviewers. This work was supported by the Blue Waters Project, National Science Foundation, National Geospatial-Intelligence Agency, and Fiddler Endowment.; 2021 IEEE Visualization Conference, VIS 2021 ; Conference date: 24-10-2021 Through 29-10-2021",
year = "2021",
doi = "10.1109/VIS49827.2021.9623327",
language = "English (US)",
series = "Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021",
address = "United States",
}