The Final Frontier: Deep Learning in Space
Abstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most studies surveying AI with respect to space applications have either been reviews concerning closely related aspects of space missions or detailed topical reviews on specific AI techniques or algorithms to a specific field.
“Summarily, most studies surveying AI with respect to space applications have either been reviews concerning closely related aspects of space missions (([5], [7]) or detailed topical reviews with respect to the development and application of specific AI techniques or algorithms ([15], [11] and [16]) to a specific field ([17]).”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [15]
Parts of an image of no interest can be discarded to drastically reduce the amount of data transmitted.
“Parts of the image of no interest (e.g. those occluded by clouds) can be discarded so as to drastically reduce the amount of data transmitted [139],[15].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [15]
Cites
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215 · cited 2× · see citing works
“The uptake of off-the-shelf components, would allows us to leverage recent developments in compute efficient (quantized) [40], memory sensitive (compressed) [26], and energy aware [46] terrestrial DL models.”
BackgroundDeep learning classifiers for hyperspectral imaging: A review
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.09.006 · Cited by 1,094 · see citing works
International Journal of Remote Sensing
(2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2012.638042 · Cited by 1,000 · see citing works
Squeezing Deep Learning into Mobile and Embedded Devices
Lane, N. D., Bhattacharya, S., Mathur, A., Georgiev, P., Forlivesi, C., & Kawsar, F. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.2940968 · Cited by 224 · see citing works
Self-citationUsing Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery
Wimmers, A., Velden, C. S., & Cossuth, J. H. (2019) · Monthly Weather Review · doi:10.1175/mwr-d-18-0391.1 · Cited by 168 · see citing works
Reinforcement Learning for Satellite Communications: From LEO to Deep Space Operations
Ferreira, P. V. R., Paffenroth, R., Wyglinski, A. M., Hackett, T. M., Bilen, S. G., Reinhart, R. C., & Mortensen, D. J. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800796 · Cited by 82 · see citing works
Near lossless data compression onboard a hyperspectral satellite
Qian, S., Bergeron, M., Cunningham, I., Gagnon, L., & Hollinger, A. (2006) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2006.248183 · Cited by 46 · see citing works
Neural networks on microcontrollers: saving memory at inference via operator reorderingPreprint
Liberis, E., & Lane, N. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05110 · Cited by 40 · see citing works
Self-citationSynthetic Aperture Radar (SAR): Principles and Applications
Moreira, A. (2013) · elib (German Aerospace Center) · Cited by 22 · see citing works
Satellite Identification Imaging for Small Satellites Using NVIDIA
Buonaiuto, N., Louie, M., Aarestad, J., Mital, R., Mateik, D., Sivilli, R., Bhopale, A., Kief, C. J., & Zufelt, B. (2017) · Digital Commons - USU (Utah State University) · Cited by 18 · see citing works
Ortíz, F., Rodríguez‐Osorio, R. M., Salas‐Natera, M. A., & Ayala, S. L. (2019) · Institution of Engineering and Technology eBooks · doi:10.1049/pbte086e_ch12 · Cited by 7 · see citing works
On the use of neural networks for flexible payload management in VHTS systems
Ortíz, F., Rodríguez‐Osorio, R. M., Salas‐Natera, M. A., Salvador, L.-A., Tarchi, D., & Vanelli‐Coralli, A. (2019) · Hispana · Cited by 7 · see citing works
Increasing Spacecraft Autonomy through Embedded Neural Networks for Semantic Image Analysis
Schartel, A. (2017) · Epubl LTU · Cited by 3 · see citing works
Image dequantization for hyperspectral lossy compression with convolutional neural networks
Valsesia, D., & Magli, E. (2019) · PORTO@Iris · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Furano, G., Meoni, G., Dunne, A., Moloney, D., Ferlet-Cavrois, V., Tavoularis, A., Byrne, J., Buckley, L., Psarakis, M., Voss, K.-O., et al. (2020) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2020.3008468 · Cited by 259 · see citing works
Giuffrida, G., Fanucci, L., Meoni, G., Batič, M., Buckley, L., Dunne, A. K., van Dijk, C., Esposito, M., Hefele, J., Vercruyssen, N., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3125567 · Cited by 231 · see citing works
Artificial intelligence for satellite communication: A review
Fourati, F., & Alouini, M. (2021) · Intelligent and Converged Networks · doi:10.23919/icn.2021.0015 · Cited by 197 · see citing works
Cloudscout: a deep neural network for on-board cloud detection on hyperspectral images
Giuffrida, G., Diana, L., de Gioia, F., Benelli, G., Meoni, G., Donati, M., & Fanucci, L. (2020) · Remote Sensing · doi:10.3390/rs12142205 · Cited by 176 · see citing works
Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
Satellite Computing: Vision and Challenges
Wang, S., & Li, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303346 · Cited by 113 · see citing works
Deep learning-based spacecraft relative navigation methods: A survey
Song, J., Rondao, D., & Aouf, N. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2021.10.025 · Cited by 106 · see citing works
“[4] collated various applications of DL for space, briefly discussing the achieved and prospective goals of 4”
Kua, J., Loke, S. W., Arora, C., Fernando, N., & Ranaweera, C. (2021) · Sensors · doi:10.3390/s21238117 · Cited by 109 · see citing works
Confidence Score: The Forgotten Dimension of Object Detection Performance Evaluation
Wenkel, S., Alhazmi, K., Liiv, T., Alrshoud, S. R., & Simón, M. (2021) · Sensors · doi:10.3390/s21134350 · Cited by 102 · see citing works
A Survey of Next-generation Computing Technologies in Space-air-ground Integrated Networks
Shen, Z., Jin, J., Tan, C., Tagami, A., Wang, S., Li, Q., Zheng, Q., & Yuan, J. (2023) · ACM Computing Surveys · doi:10.1145/3606018 · Cited by 84 · see citing works
Rapuano, E., Meoni, G., Pacini, T., Dinelli, G., Furano, G., Giuffrida, G., & Fanucci, L. (2021) · Remote Sensing · doi:10.3390/rs13081518 · Cited by 77 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“Historically the majority of the automated control and navigation maneuvering operations were directed by a human, which is burdensome and only attainable for near Earth missions [15].”
BackgroundInfluentialToward Resilient Network Slicing for Satellite–Terrestrial Edge Computing IoT
Esmat, H. H., Lorenzo, B., & Shi, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3277466 · Cited by 63 · see citing works
RaVÆn: unsupervised change detection of extreme events using ML on-board satellites
Růžička, V., Vaughan, A., Martini, D. D., Fulton, J., Salvatelli, V., Bridges, C., Mateo‐García, G., & Zantedeschi, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-19437-5 · Cited by 49 · see citing works
Miralles, P., Thangavel, K., Scannapieco, A. F., Jagadam, N., Baranwal, P., Faldu, B., Abhang, R., Bhatia, S., Bonnart, S., Bhatnagar, I., et al. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.02.025 · Cited by 43 · see citing works
Artificial Intelligence Based On-Board Image Compression for the Φ-Sat-2 Mission
Guerrisi, G., Frate, F. D., & Schiavon, G. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3296485 · Cited by 40 · see citing works
Kucik, A., & Meoni, G. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00230 · Cited by 36 · see citing works
Automated Classification of Plasma Regions Using 3D Particle Energy Distributions
Olshevsky, V., Khotyaintsev, Y. V., Lalti, A., Divin, A., Delzanno, G. L., Anderzén, S., Herman, P. A., Chien, S. W. D., Avanov, L., Dimmock, A. P., et al. (2021) · Journal of Geophysical Research Space Physics · doi:10.1029/2021ja029620 · Cited by 33 · see citing works
LWCDnet: A Lightweight Network for Efficient Cloud Detection in Remote Sensing Images
Luo, C., Feng, S., Yang, X., Ye, Y., Li, X., Zhang, B., Chen, Z., & Quan, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3173661 · Cited by 32 · see citing works
Massimi, F., Ferrara, P., & Benedetto, F. (2022) · Sensors · doi:10.3390/s23010124 · Cited by 27 · see citing works
Lai, Z., Li, H., Zhang, Q., Wu, Q., & Wu, J. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3260166 · Cited by 24 · see citing works
Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks
Guidi, T., Python, L., Forasassi, M., Cucci, C., Franci, M., Argenti, F., & Barucci, A. (2023) · Algorithms · doi:10.3390/a16020079 · Cited by 23 · see citing works
Lai, Z., Li, H., Zhang, Q., Wu, Q., & Wu, J. (2021) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp52444.2021.9651950 · Cited by 22 · see citing works
Lai, Z., Li, H., Wu, Q., Ni, Q., Lv, M., Li, J., Wu, J., Liu, J., & Li, Y. (2022) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2022.3221391 · Cited by 21 · see citing works
Deep-Learning-Based System for Change Detection Onboard Earth Observation Small Satellites
Serief, C., Ghelamallah, Y., & Bentoutou, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3284919 · Cited by 20 · see citing works
A Survey on Satellite Computing: Connecting the Dots Between Networks and Applications
Guimarães, C., Netti, A., Sauer, M. S., Zeiger, F., Huth, H., & Boriskova, E. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3579525 · Cited by 18 · see citing works
Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Justo, J. A., Ghiţă, A., Kováč, D., Garrett, J. L., Georgescu, M.-I., González-Llorente, J., Ionescu, R. T., & Johansen, T. A. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3487360 · Cited by 16 · see citing works
del Castillo, M. O., Morgan, J., McRobbie, J., Therakam, C., Joukhadar, Z., Mearns, R., Barraclough, S., Sinnott, R., Woods, A., Bayliss, C., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00672 · Cited by 16 · see citing works
Pieczyński, D., Ptak, B., Kraft, M., & Drapikowski, P. (2023) · Applied Sciences · doi:10.3390/app132212401 · Cited by 15 · see citing works
Communications for the Planet Mars: Past, Present, and Future
Koktas, E., & Başar, E. (2024) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2024.3396117 · Cited by 15 · see citing works
Cornejo, J., García, C., & Baca, J. (2024) · Biomimetics · doi:10.3390/biomimetics9110693 · Cited by 14 · see citing works
Driver, T., Skinner, K. A., Dor, M., & Panagiotis Tsiotras, B. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.01.009 · Cited by 13 · see citing works
Exploring Real-Time Satellite Computing: From Energy and Thermal Perspectives
Li, Q., Wang, S., Xu, C., Ma, X., Xu, M., Zhou, A., Xing, R., Yang, B., Zhu, Z., Zhang, Y., et al. (2024) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss62706.2024.00023 · Cited by 13 · see citing works
ICU4SAT: A General-Purpose Reconfigurable Instrument Control Unit Based on Open Source Components
Nannipieri, P., Giuffrida, G., Diana, L., Panicacci, S., Zulberti, L., Fanucci, L., Hernandez, H. G. M., & Hübner, M. (2022) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero53065.2022.9843414 · Cited by 11 · see citing works
Using Machine Learning to Mitigate Single-Event Upsets in RF Circuits and Systems
Ildefonso, A., Kimball, J. P., Khachatrian, A., Mensah, Y. A., Teng, J. W., Tzintzarov, G. N., Rao, S. G., Moradinia, A., Cressler, J. D., & McMorrow, D. (2021) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2021.3125794 · Cited by 11 · see citing works
Kim, T., Jeon, M., Lee, C., Kim, J., Ko, G., Kim, J.-Y., & Youn, C. (2022) · IEEE Access · doi:10.1109/access.2022.3219879 · Cited by 11 · see citing works
FPGA based Adaptive Hardware Acceleration for Multiple Deep Learning Tasks
Lu, Y., Zhai, X., Saha, S., Ehsan, S., & McDonald-Maier, K. D. (2021) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc51149.2021.00038 · Cited by 10 · see citing works
Remote Sensing Imagery Object Detection Model Compression via Tucker Decomposition
Huyan, L., Li, Y., Jiang, D., Zhang, Y., Zhou, Q., Li, B., Wei, J., Liu, J., Zhang, Y., Wang, P., et al. (2023) · Mathematics · doi:10.3390/math11040856 · Cited by 9 · see citing works
GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
Benelli, G., Todaro, G., Monopoli, M., Giuffrida, G., Donati, M., & Fanucci, L. (2024) · Electronics · doi:10.3390/electronics13193928 · Cited by 9 · see citing works
Investigating the impact of transient hardware faults on deep learning neural network inference
Rahman, M. H., Laskar, S., & Li, G. (2024) · Software Testing Verification and Reliability · doi:10.1002/stvr.1873 · Cited by 9 · see citing works
AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload
Ekelund, J., Vinuesa, R., Khotyaintsev, Y., Henri, P., Delzanno, G., & Markidis, S. (2024) · IEEE International Conference on e-Science · doi:10.1109/e-science62913.2024.10678688 · Cited by 10 · see citing works
Sharma, E., Davey, C. P., Deo, R. C., Carter, B., & Salcedo‐Sanz, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11317-4 · Cited by 8 · see citing works
Shan, D., Wang, X., Kim, D. K., Yang, Z., Lang, S., Cai, D., Wang, W., & Gao, X. (2025) · Space Science & Technology · doi:10.34133/space.0216 · Cited by 7 · see citing works
Convolutional Autoencoder Algorithm for On-Board Image Compression
Guerrisi, G., Frate, F. D., & Schiavon, G. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883256 · Cited by 7 · see citing works
Ciardi, R., Giuffrida, G., Benelli, G., Cardenio, C., & Maderna, R. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-25755-1_3 · Cited by 7 · see citing works
Exploiting augmented intelligence in the modeling of safety-critical autonomous systems
Yang, Z.-B., Bao, Y., Yang, Y., Huang, Z.-Q., Bodeveix, J., Filali, M., & Gu, Z. (2021) · Formal Aspects of Computing · doi:10.1007/s00165-021-00543-6 · Cited by 10 · see citing works
Pacini, T., Rapuano, E., Tuttobene, L., Nannipieri, P., Fanucci, L., & Moranti, S. (2023) · European Data Handling & Data Processing Conference (EDHPC) · doi:10.23919/edhpc59100.2023.10396607 · Cited by 7 · see citing works
Machine Learning in Space: Surveying the Robustness of On-Board ML Models to Radiation
Lange, K., Fontana, F., Rossi, F., Varile, M., & Apruzzese, G. (2024) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc61854.2024.00012 · Cited by 5 · see citing works
Kotian, A. L., Sheik, A., Akhilesh, H. R., B, A. R., & Amrutha, M. (2025) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca65293.2025.11089903 · Cited by 5 · see citing works
3D Component Segmentation Network and Dataset for Non-Cooperative Spacecraft
Zhao, G., Wan, X., Tian, Y., Shao, Y., & Li, S. (2022) · Aerospace · doi:10.3390/aerospace9050248 · Cited by 5 · see citing works
Profiling Vision-based Deep Learning Architectures on NASA SpaceCube Platforms
Chase, T. B., Goodwill, J. S., Dantu, K., & Wilson, C. (2024) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero58975.2024.10521096 · Cited by 5 · see citing works
Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level
Ibrahim, Y., Liu, J., Yang, X., Sha, H., Li, P., & Wang, H. (2020) · WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL · doi:10.37394/23203.2020.15.70 · Cited by 5 · see citing works
Training Deep Learning Spacecraft Component Detection Algorithms Using Synthetic Image Data
Viggh, H. E. M., Loughran, S., Rachlin, Y., Allen, R. E., & Ruprecht, J. (2023) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero55745.2023.10115578 · Cited by 5 · see citing works
Ground-Assisted LEO Satellite Federated Learning: Dynamic, Efficient, Distributed Learning
Zhang, F., Wang, D., Kang, J., & Niyato, D. T. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3593252 · Cited by 4 · see citing works
Applications of AI in Space Domain
dos Santos, L. B. R., Basciani, F., & Pelliccione, P. (2025) · ACM Computing Surveys · doi:10.1145/3757317 · Cited by 4 · see citing works
Technologies and Infrastructures for a Sustainable Space
Cianca, E., Morosi, S., & Ruggieri, M. (2023) · Signals and communication technology · doi:10.1007/978-3-031-30762-1_8 · Cited by 4 · see citing works
LGCNet: A Cloud Detection Method in Remote Sensing Images Using Local and Global Semantics
Chen, L., Feng, S., Wang, H., Zhang, B., Yao, P., Luo, C., Ye, Y., Xu, Y. D., Li, X., & Fang, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3415618 · Cited by 4 · see citing works
Castilla-Arquillo, R., Pérez-del-Pulgar, C. J., Paz-Delgado, G. J., & Gerdes, L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3219306 · Cited by 4 · see citing works
Guerrisi, G., Schiavon, G., & Frate, F. D. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10281562 · Cited by 4 · see citing works
A Lightweight Model for Satellite Pose Estimation
Carcagnì, P., Leo, M., Spagnolo, P., Mazzeo, P. L., & Distante, C. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-06427-2_1 · Cited by 6 · see citing works
Gruchola, S., Schmidt, P. K., Tulej, M., Riedo, A., Mezger, K., & Wurz, P. (2024) · The Planetary Science Journal · doi:10.3847/psj/ad90b6 · Cited by 3 · see citing works
Kondrateva, O., Dietzel, S., Lößer, A., & Scheuermann, B. (2023) · Mediterranean Communication and Computer Networking Conference (MedComNet) · doi:10.1109/medcomnet58619.2023.10168858 · Cited by 3 · see citing works
FPGA-Based Hyperspectral Lossy Compressor With Adaptive Distortion Feature for Unexpected Scenarios
Caba, J., Stroobandt, D., Díaz, M., Barba, J., Rincón, F., López, S., & López, J. C. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3298484 · Cited by 3 · see citing works
AMANet: An Adaptive Memory Attention Network for video cloud detection
Luo, C., Feng, S., Quan, Y., Ye, Y., Xu, Y. D., Li, X., & Zhang, B. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110616 · Cited by 3 · see citing works
Kondrateva, O., Dietzel, S., & Scheuermann, B. (2024) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking62109.2024.10619868 · Cited by 2 · see citing works
Space Computing Constellation: System Architecture, Implementations, and Challenges
Wang, H., Yao, K., Gong, L., Jin, Y., Zhang, P., Liu, Y., Yuan, Y., Xue, J., Wan, Z., Liu, Y., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3648643 · Cited by 3 · see citing works
A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies
Lutepo, A., & Zhang, K. (2024) · Communications and Network · doi:10.4236/cn.2024.163005 · Cited by 2 · see citing works
Evil from Within: Machine Learning Backdoors Through Dormant Hardware Trojans
Warnecke, A., Speith, J., Möller, J.-N., Rieck, K., & Paar, C. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00077 · Cited by 2 · see citing works
Assessing the Potential of Qormino Processor for Embedded AI on Board a CubeSat
Vandame, P., Noe, A., Čech, J., Apostol, L., Prieur, C., McNamara, K., Martin, F., Sequies, T., Mcnamara, T., Barthélémy, M., et al. (2022) · IEEE Journal on Miniaturization for Air and Space Systems · doi:10.1109/jmass.2022.3202438 · Cited by 2 · see citing works
Intelligent Space Communication Networks
Marchese, M., Morosi, S., & Patrone, F. (2023) · Signals and communication technology · doi:10.1007/978-3-031-30762-1_7 · Cited by 2 · see citing works
CNN-based On-board Intelligent Processing for Remote Sensing Images Interpretation
Cui, X., Liu, Y., Chen, X., & Wang, G. (2023) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml60161.2023.10424867 · Cited by 1 · see citing works
Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites
Plumridge, M., Maråk, R., Ceccobello, C., Gómez, P., Meoni, G., Svoboda, F., & Lane, N. D. (2025) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas63548.2025.10924497 · Cited by 1 · see citing works
Self-citationHardware Accelerators for Hyperspectral Image Classification in Space
Viel, F., Zeferino, C. A., & Bezerra, E. A. (2025) · Intelligent systems reference library · doi:10.1007/978-3-031-83123-2_3 · Cited by 1 · see citing works
Chen, C.-F., Lee, P., Chen, C.-H., & Bergies, S. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3661035 · Cited by 1 · see citing works
Analysis of single event induced bit faults in a deep neural network accelerator pipeline
Jonckers, N., Vinck, T., Karsmakers, P., & Prinzie, J. (2025) · Journal of Instrumentation · doi:10.1088/1748-0221/21/02/c02021 · Cited by 3 · see citing works
Ferrari, L., Dell’Acqua, F., Zhang, P., & Du, P. (2021) · Remote Sensing · doi:10.3390/rs13214361 · Cited by 1 · see citing works
A Hitchhiker’s Guide to Neural Network Design for Onboard Deployment in Space
Benoot, W. (2023) · European Data Handling & Data Processing Conference (EDHPC) · doi:10.23919/edhpc59100.2023.10396591 · Cited by 1 · see citing works
A Novel Method for Rapid Orbital Deployment of ML for Space Applications
Hall, S., Jayaraman, P. P., & Moar, P. (2024) · IEEE Space Computing Conference (SCC) · doi:10.1109/scc61854.2024.00007 · Cited by 1 · see citing works
On the Application of Machine Learning to Planetary Sciences
Cambioni, S. (2020) · UA Campus Repository (The University of Arizona)
A Generalized Software Framework for Deep Neural Network Inference in Space Applications
Segovia, R. P., Zemerick, S. A., & Mehta, P. M. (2025) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2025.3574111
On‐Orbit Fast Small Target Detection Method Based on the Improved YOLOv5 Model
Li, Q., & Zhu, Z. (2025) · International Journal of Aerospace Engineering · doi:10.1155/ijae/5533761
On-orbit model training for satellite imagery with label proportions
Ramos-Pollán, R., & Gonzalez, F. A. (2025) · European Journal of Remote Sensing · doi:10.1080/22797254.2025.2570319
Thermal-Aware Scheduling of Hybrid Mission Workflows in Software-Defined Satellites
Fan, X., & Li, X. (2025) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1109/acai68217.2025.11406370
Case Studies in Verifying Spacecraft Autonomy
Feather, M. S., Prokop, L. E., & Dorney, D. J. (2026) · Springer eBooks · doi:10.1007/978-3-031-88546-4_10
Jamshed, M. A., Haq, B., Saad, M. M., Mohsin, M. A., & Lin, X. (2026) · npj Wireless Technology · doi:10.1038/s44459-026-00037-y
ORBIT: Meta-Learned Online Convex Optimization for Low-Latency In-Orbit Computing
Cao, C. (2026) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon68281.2026.11579131
- Authors
- Kothari, V., Liberis, E., & Lane, N. D.
- Affiliations
- University of Oxford (GB)
- Venue
- Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications
- Publisher
- ACM
- Published
- 2020-03-03
- Pages
- 45-49
- ISSN
- 1550-6193 (ISSN-L)
- OpenAlex
- W3007493000
- Cited by
- 86
- References
- 15
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 17 · 2023: 22 · 2022: 11 · 2021: 14 · 2020: 4
- FWCI
- 3.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) (publishedVersion)
- Funders
- EPSRC (DTP (EP/R513295/1), MOA (EP/S001530/))
- Samsung AI
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Space Technology and Applications
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Design and Technology
- Keywords
- on-device deep learning, embedded machine learning, satellite imaging, spacecraft navigation, communication cost reduction, modular satellites, commercial space launch
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
