[2506.03938] FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review

Computer Science > Machine Learning arXiv:2506.03938 (cs) [Submitted on 4 Jun 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review Authors:Cédric Léonard (1 and 2), Dirk Stober (1), Martin Schulz (1) ((1) Technical University of Munich, Munich, Germany, (2) Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Weßling, Germany) View a PDF of the paper titled FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review, by C\'edric L\'eonard (1 and 2) and 7 other authors View PDF HTML (experimental) Abstract: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 this https URL. Comments: Subjects: Machine Learning (cs.LG); Hardware Archite...