# Single Image Cloud Detection via Multi-Image Fusion

Source: [Single Image Cloud Detection via Multi-Image Fusion](<https://www.sota2.com/research/paper/68fa680a1e209131cea19b8a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:27:00.192Z

Authors: Scott Workman, M. Usman Rafique, Hunter Blanton, Connor Greenwell, Nathan Jacobs

Published: 2020-07-29T22:52:28.000Z

## Abstract

Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we explore how recent advances in multi-image fusion can be leveraged to bootstrap single image cloud detection. We demonstrate that a network optimized to estimate image quality also implicitly learns to detect clouds. To support the training and evaluation of our approach, we collect a large dataset of Sentinel-2 images along with a per-pixel semantic labelling for land cover. Through various experiments, we demonstrate that our method reduces the need for annotated training data and improves cloud detection performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.15144>)

[PDF](<https://arxiv.org/pdf/2007.15144.pdf>)