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

TL;DR

This paper introduces CloudFindr, a deep learning-based method using U-Net to effectively mask and remove cloud artifacts from single-channel satellite DEM data, improving visualization and analysis.

Contribution

It presents a novel approach combining traditional image processing with deep learning that works on single-channel DEMs, unlike previous multi-channel dependent methods.

Findings

Effective cloud artifact masking on DEMs

Does not require multi-channel spectral data

Applicable to various Earth science applications

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.