mlsanityData to 7 Oct 2026

The Final Frontier: Deep Learning in Space

arXiv 2001.10362 ↗First posted 27 Jan 2020eess.SP cs.AI cs.SYcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 4; 2025: 5; 2026: 1.

21 papers.

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  1. 2023A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospectscited by 30
  2. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  3. 2021Deep Learning-based Spacecraft Relative Navigation Methods: A Surveycited by 17
  4. 2021Artificial Intelligence for Satellite Communication: A Reviewcited by 14
  5. 2022AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learningcited by 10
  6. 2023Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learningcited by 5
  7. 2024Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRITcited by 3
  8. 2024Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiationcited by 3
  9. 2024AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Uploadcited by 2
  10. 2025FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Reviewcited by 2
  11. 2023On-orbit model training for satellite imagery with label proportionscited by 1
  12. 2025Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENscited by 1
9 more
  1. 2021Coping with Mistreatment in Fair Algorithmscited by 0
  2. 2022Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networkscited by 0
  3. 2022Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationscited by 0
  4. 2023Manifold Regularization for Memory-Efficient Training of Deep Neural Networkscited by 0
  5. 2024Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellitescited by 0
  6. 2025Sun sensor calibration algorithms: A systematic mapping and surveycited by 0
  7. 2025Guidance and Control Neural Network Acceleration using Memristorscited by 0
  8. 2025SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusioncited by 0
  9. 2026Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGAcited by 0