# The Final Frontier: Deep Learning in Space

arXiv: https://arxiv.org/abs/2001.10362 · HTML: https://mlsanity.com/paper/2001.10362/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 21 of 21)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [The Neural Process Family: Survey, Applications and Perspectives](https://mlsanity.com/paper/2209.00517/) (2022)
- [Deep Learning-based Spacecraft Relative Navigation Methods: A Survey](https://mlsanity.com/paper/2108.08876/) (2021)
- [Artificial Intelligence for Satellite Communication: A Review](https://mlsanity.com/paper/2101.10899/) (2021)
- [AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning](https://mlsanity.com/paper/2208.02053/) (2022)
- [Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning](https://mlsanity.com/paper/2310.16210/) (2023)
- [Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT](https://mlsanity.com/paper/2404.08399/) (2024)
- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload](https://mlsanity.com/paper/2406.14297/) (2024)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [On-orbit model training for satellite imagery with label proportions](https://mlsanity.com/paper/2306.12461/) (2023)
- [Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs](https://mlsanity.com/paper/2509.04506/) (2025)
- [Coping with Mistreatment in Fair Algorithms](https://mlsanity.com/paper/2102.10750/) (2021)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations](https://mlsanity.com/paper/2206.02622/) (2022)
- [Manifold Regularization for Memory-Efficient Training of Deep Neural Networks](https://mlsanity.com/paper/2305.17119/) (2023)
- [Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites](https://mlsanity.com/paper/2411.17831/) (2024)
- [Sun sensor calibration algorithms: A systematic mapping and survey](https://mlsanity.com/paper/2507.21541/) (2025)
- [Guidance and Control Neural Network Acceleration using Memristors](https://mlsanity.com/paper/2509.02369/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)
- [Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGA](https://mlsanity.com/paper/2608.11271/) (2026)

## Abstract (arXiv)

Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a space device, e.g. a satellite, is becoming more accessible to small actors due to the development of modular satellites and commercial space launches, which fuels further growth of this area. Deep learning's ability to deliver sophisticated computational intelligence makes it an attractive option to facilitate various tasks on space devices and reduce operational costs. In this work, we identify deep learning in space as one of development directions for mobile and embedded machine learning. We collate various applications of machine learning to space data, such as satellite imaging, and describe how on-device deep learning can meaningfully improve the operation of a spacecraft, such as by reducing communication costs or facilitating navigation. We detail and contextualise compute platform of satellites and draw parallels with embedded systems and current research in deep learning for resource-constrained environments.