Research Topic

AI in the Space Sciences

About this Research Topic

The space sciences encompass the scientific disciplines related to the exploration and study of space, as well as more Earth-oriented applications such as remote sensing and atmospheric monitoring. The use of artificial intelligence (AI) in the space sciences is rapidly expanding and data generated by current and upcoming space missions will allow scientists to probe deeper into the cosmos, as well as gain new insights about our planet and how we shape it.

The massive amount of data now being delivered in real-time by Earth observation assets (e.g., the Copernicus sentinel network) and astronomical missions (e.g., Kepler Space Telescope) is providing exciting new datasets and has necessitated the adoption of novel machine learning techniques. Indeed, remote sensing is driving innovation in deep learning for large-scale image processing tasks, whereas in astrophysics AI is being used in a broad range of applications, including planet detection, galaxy classification, and cosmological modeling.

Less theoretical applications in the space sciences are also benefiting from the adoption of AI. Mission planners are turning to machine learning techniques in order to optimize spacecraft trajectories, saving fuel and extending the lifetime of missions. Closer to home, scientists are locating space debris and potentially devastating near-Earth objects with increasing frequency. Solar physicists are using AI to predict the magnitude and direction of solar flares, which—if undetected—can cause billions of dollars in damage to satellite constellations, power grids, and telecommunication networks around the world.

In order to highlight the uses of AI in space, this article collection welcomes contributions related to the use of AI in space or to study space. This article collection is a follow-on initiative of the "AI & Space" track, held at the Applied Machine Learning Days 2020 in Lausanne from 25-29 January, 2020. Examples of topics of interest relevant to this article collection include, but are not limited to:

• Astronomy & astrophysics
• Autonomous spacecraft operation
• Earth observation / Remote sensing
• Planetary science
• Physics-aware AI
• Space-based atmospheric monitoring
• Space mission planning & operation
• Space-borne robotics

The contributions presented at the "AI & Space" track are particularly welcome in this Research Topic. Additionally, other contributions fitting in the scope of the topic as outlined above are also encouraged.


Keywords: AI, space sciences, astrophysics, big data, artificial intelligence, spacecraft operation, planetary science, space missions


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The space sciences encompass the scientific disciplines related to the exploration and study of space, as well as more Earth-oriented applications such as remote sensing and atmospheric monitoring. The use of artificial intelligence (AI) in the space sciences is rapidly expanding and data generated by current and upcoming space missions will allow scientists to probe deeper into the cosmos, as well as gain new insights about our planet and how we shape it.

The massive amount of data now being delivered in real-time by Earth observation assets (e.g., the Copernicus sentinel network) and astronomical missions (e.g., Kepler Space Telescope) is providing exciting new datasets and has necessitated the adoption of novel machine learning techniques. Indeed, remote sensing is driving innovation in deep learning for large-scale image processing tasks, whereas in astrophysics AI is being used in a broad range of applications, including planet detection, galaxy classification, and cosmological modeling.

Less theoretical applications in the space sciences are also benefiting from the adoption of AI. Mission planners are turning to machine learning techniques in order to optimize spacecraft trajectories, saving fuel and extending the lifetime of missions. Closer to home, scientists are locating space debris and potentially devastating near-Earth objects with increasing frequency. Solar physicists are using AI to predict the magnitude and direction of solar flares, which—if undetected—can cause billions of dollars in damage to satellite constellations, power grids, and telecommunication networks around the world.

In order to highlight the uses of AI in space, this article collection welcomes contributions related to the use of AI in space or to study space. This article collection is a follow-on initiative of the "AI & Space" track, held at the Applied Machine Learning Days 2020 in Lausanne from 25-29 January, 2020. Examples of topics of interest relevant to this article collection include, but are not limited to:

• Astronomy & astrophysics
• Autonomous spacecraft operation
• Earth observation / Remote sensing
• Planetary science
• Physics-aware AI
• Space-based atmospheric monitoring
• Space mission planning & operation
• Space-borne robotics

The contributions presented at the "AI & Space" track are particularly welcome in this Research Topic. Additionally, other contributions fitting in the scope of the topic as outlined above are also encouraged.


Keywords: AI, space sciences, astrophysics, big data, artificial intelligence, spacecraft operation, planetary science, space missions


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 June 2020 Abstract
30 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 June 2020 Abstract
30 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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