# A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

arXiv: https://arxiv.org/abs/1903.04538 · HTML: https://mlsanity.com/paper/1903.04538/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 26 of 26)

- [PixelSynth: Generating a 3D-Consistent Experience from a Single Image](https://mlsanity.com/paper/2108.05892/) (2021)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory](https://mlsanity.com/paper/2210.06478/) (2022)
- [High Resolution Solar Image Generation using Generative Adversarial Networks](https://mlsanity.com/paper/2106.03814/) (2021)
- [Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation](https://mlsanity.com/paper/2208.09512/) (2022)
- [Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification](https://mlsanity.com/paper/2103.17273/) (2021)
- [Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme](https://mlsanity.com/paper/2511.19390/) (2025)
- [SCSS-Net: Solar Corona Structures Segmentation by Deep Learning](https://mlsanity.com/paper/2109.10834/) (2021)
- [Solaris: A Foundation Model of the Sun](https://mlsanity.com/paper/2411.16339/) (2024)
- [SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction](https://mlsanity.com/paper/2508.14107/) (2025)
- [SG-PALM: a Fast Physically Interpretable Tensor Graphical Model](https://mlsanity.com/paper/2105.12271/) (2021)
- [SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory](https://mlsanity.com/paper/2108.12421/) (2021)
- [A Foundation Model for the Solar Dynamics Observatory](https://mlsanity.com/paper/2410.02530/) (2024)
- [AI Foundation Model for Heliophysics: Applications, Design, and Implementation](https://mlsanity.com/paper/2410.10841/) (2024)
- [Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles](https://mlsanity.com/paper/2112.09051/) (2021)
- [Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges](https://mlsanity.com/paper/2409.04850/) (2024)
- [Using Multiple Instance Learning for Explainable Solar Flare Prediction](https://mlsanity.com/paper/2203.13896/) (2022)
- [Multi-spectral Entropy Constrained Neural Compression of Solar Imagery](https://mlsanity.com/paper/2309.10791/) (2023)
- [Neural-based Compression Scheme for Solar Image Data](https://mlsanity.com/paper/2311.02855/) (2023)
- [High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery](https://mlsanity.com/paper/2312.06845/) (2023)
- [A Comparative Study on Generative Models for High Resolution Solar Observation Imaging](https://mlsanity.com/paper/2304.07169/) (2023)
- [Context-Aware Neural Video Compression on Solar Dynamics Observatory](https://mlsanity.com/paper/2309.10784/) (2023)
- [Neural-based Video Compression on Solar Dynamics Observatory Images](https://mlsanity.com/paper/2407.15730/) (2024)
- [Spectral and Spatial Graph Learning for Multispectral Solar Image Compression](https://mlsanity.com/paper/2512.24463/) (2025)
- [Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference](https://mlsanity.com/paper/2603.14091/) (2026)
- [PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images](https://mlsanity.com/paper/2609.26683/) (2026)

## Abstract (arXiv)

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.