# Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge

arXiv: https://arxiv.org/abs/2212.13289 · HTML: https://mlsanity.com/paper/2212.13289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 26 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics](https://mlsanity.com/paper/1905.13575/) (2019)

## Abstract (arXiv)

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.