# Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder

arXiv: https://arxiv.org/abs/2103.04885 · HTML: https://mlsanity.com/paper/2103.04885/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 81 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Hybrid Models with Deep and Invertible Features](https://mlsanity.com/paper/1902.02767/) (2019)
- [Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search](https://mlsanity.com/paper/1803.00370/) (2018)
- [Cumulo: A Dataset for Learning Cloud Classes](https://mlsanity.com/paper/1911.04227/) (2019)
- [Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection](https://mlsanity.com/paper/1906.01906/) (2019)
- [Transform Invariant Auto-encoder](https://mlsanity.com/paper/1709.03754/) (2017)

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

- [AICCA: AI-driven Cloud Classification Atlas](https://mlsanity.com/paper/2209.15096/) (2022)
- [Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder](https://mlsanity.com/paper/2211.00860/) (2022)

## Abstract (arXiv)

Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence. These datasets open up the possibility of improved understanding of cloud dynamics and feedback, which remain the biggest source of uncertainty in global climate model projections. As a step towards answering these questions, we describe an automated rotation-invariant cloud clustering (RICC) method that leverages deep learning autoencoder technology to organize cloud imagery within large datasets in an unsupervised fashion, free from assumptions about predefined classes. We describe both the design and implementation of this method and its evaluation, which uses a sequence of testing protocols to determine whether the resulting clusters: (1) are physically reasonable, (i.e., embody scientifically relevant distinctions); (2) capture information on spatial distributions, such as textures; (3) are cohesive and separable in latent space; and (4) are rotationally invariant, (i.e., insensitive to the orientation of an image). Results obtained when these evaluation protocols are applied to RICC outputs suggest that the resultant novel cloud clusters capture meaningful aspects of cloud physics, are appropriately spatially coherent, and are invariant to orientations of input images. Our results support the possibility of using an unsupervised data-driven approach for automated clustering and pattern discovery in cloud imagery.