# Cloud Classification with Unsupervised Deep Learning

arXiv: https://arxiv.org/abs/2209.15585 · HTML: https://mlsanity.com/paper/2209.15585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)

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

- [Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder](https://mlsanity.com/paper/2211.00860/) (2022)
- [Application of Tensorized Neural Networks for Cloud Classification](https://mlsanity.com/paper/2405.10946/) (2024)

## Abstract (arXiv)

We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies. While previous neural network-based cloud classification models have used supervised learning methods, unsupervised learning allows us to avoid restricting the model to artificial categories based on historical cloud classification schemes and enables the discovery of novel, more detailed classifications. Our framework learns cloud features directly from radiance data produced by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument, deriving cloud characteristics from millions of images without relying on pre-defined cloud types during the training process. We present preliminary results showing that our method extracts physically relevant information from radiance data and produces meaningful cloud classes.