# Cumulo: A Dataset for Learning Cloud Classes

arXiv: https://arxiv.org/abs/1911.04227 · HTML: https://mlsanity.com/paper/1911.04227/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Climate-Invariant Machine Learning](https://mlsanity.com/paper/2112.08440/) (2021)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation](https://mlsanity.com/paper/2301.09318/) (2023)
- [AICCA: AI-driven Cloud Classification Atlas](https://mlsanity.com/paper/2209.15096/) (2022)
- [Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder](https://mlsanity.com/paper/2103.04885/) (2021)
- [Interval Deep Learning for Uncertainty Quantification in Safety Applications](https://mlsanity.com/paper/2105.06438/) (2021)
- [Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder](https://mlsanity.com/paper/2211.00860/) (2022)
- [A labeled dataset of cloud types using data from GOES-16 and CloudSat](https://mlsanity.com/paper/2306.11159/) (2023)

## Abstract (arXiv)

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the Climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. To compare methods, we introduce a set of evaluation criteria, to identify models that are not only accurate, but also physically-realistic. CUMULO can be download from https://www.dropbox.com/sh/i3s9q2v2jjyk2it/AACxXnXfMF5wuIqLXqH4NJOra?dl=0 .