mlsanityData to 14 Oct 2026

FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review

arXiv 2506.03938 ↗First posted 4 Jun 2025cs.LG cs.ARcited by 2

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Abstract (arXiv)

New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large data volume, straining bandwidth and requiring onboard decision-making to transmit high-quality information in time. While Machine Learning allows real-time autonomous processing, FPGAs balance performance with adaptability to mission-specific requirements, enabling onboard deployment. This review systematically analyzes 68 experiments deploying ML models on FPGAs for Remote Sensing applications. We introduce two distinct taxonomies to capture both efficient model architectures and FPGA implementation strategies. For transparency and reproducibility, we follow PRISMA 2020 guidelines and share all data and code at https://github.com/CedricLeon/Survey_RS-ML-FPGA.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 54 of its 153 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

54 of 153 references.

References · 54

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  7. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  8. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  9. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  10. 2018YOLOv3: An Incremental Improvementcited by 2,659
  11. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  12. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
38 more
  1. 2019Searching for MobileNetV3cited by 1,994
  2. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  3. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  5. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  6. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  7. 2017Remote Sensing Image Scene Classification: Benchmark and State of the Artcited by 797
  8. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  9. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
  10. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 618
  11. 2021A White Paper on Neural Network Quantizationcited by 472
  12. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 434
  13. 2019GhostNet: More Features from Cheap Operationscited by 410
  14. 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 340
  15. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  16. 2023TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddingscited by 202
  17. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 150
  18. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 144
  19. 2019Deep Semantic Segmentation of Natural and Medical Images: A Reviewcited by 125
  20. 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 84
  21. 2019AdderNet: Do We Really Need Multiplications in Deep Learning?cited by 82
  22. 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 61
  23. 2020Rethinking Bottleneck Structure for Efficient Mobile Network Designcited by 54
  24. 2014Rigid-Motion Scattering for Texture Classificationcited by 52
  25. 2021A Review on Deep Learning in UAV Remote Sensingcited by 32
  26. 2019FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Reviewcited by 29
  27. 2019LUTNet: Rethinking Inference in FPGA Soft Logiccited by 28
  28. 2019Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagerycited by 25
  29. 2025Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategiescited by 24
  30. 2020The Final Frontier: Deep Learning in Spacecited by 21
  31. 2018DAC-SDC Low Power Object Detection Challenge for UAV Applicationscited by 18
  32. 2018Accelerating CNN inference on FPGAs: A Surveycited by 16
  33. 2018DLA: Compiler and FPGA Overlay for Neural Network Inference Accelerationcited by 15
  34. 2019Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Goingcited by 13
  35. 2023Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGAcited by 10
  36. 2025Lossy Neural Compression for Geospatial Analytics: A Reviewcited by 10
  37. 2013A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databasescited by 6
  38. 2025Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSatscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imagingcited by 0
  2. 2026Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGAcited by 0