# Self-Configuring nnU-Nets Detect Clouds in Satellite Images

arXiv: https://arxiv.org/abs/2210.13659 · HTML: https://mlsanity.com/paper/2210.13659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery](https://mlsanity.com/paper/1901.10077/) (2019)
- [Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors](https://mlsanity.com/paper/1810.05801/) (2018)
- [Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1810.05782/) (2018)
- [Automated Deep Learning: Neural Architecture Search Is Not the End](https://mlsanity.com/paper/2112.09245/) (2021)
- [SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks](https://mlsanity.com/paper/2111.08349/) (2021)

## Built on by (most cited first, 2 of 2)

- [Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning](https://mlsanity.com/paper/2310.16210/) (2023)
- [Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data](https://mlsanity.com/paper/2403.08695/) (2024)

## Abstract (arXiv)

Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can help to reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling of the cloudy areas. We approach this important task with nnU-Nets, a self-reconfigurable framework able to perform meta-learning of a segmentation network over various datasets. Our experiments, performed over Sentinel-2 and Landsat-8 multispectral images revealed that nnU-Nets deliver state-of-the-art cloud segmentation performance without any manual design. Our approach was ranked within the top 7% best solutions (across 847 participating teams) in the On Cloud N: Cloud Cover Detection Challenge, where we reached the Jaccard index of 0.882 over more than 10k unseen Sentinel-2 image patches (the winners obtained 0.897, whereas the baseline U-Net with the ResNet-34 backbone used as an encoder: 0.817, and the classic Sentinel-2 image thresholding: 0.652).