用FPGA在卫星上实时运行机器学习，提升遥感数据处理效率。
FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review
- 构建双分类体系，梳理模型架构与FPGA部署策略
- 分析68个实验，验证FPGA在遥感任务中的高效性
- 开源全部数据代码，支持可复现研究
新型无人机技术和新太空时代正重塑地球观测任务与数据获取方式。大量小型平台产生海量数据，对带宽提出挑战，并要求机载决策以及时传输高质量信息。尽管机器学习可实现实时自主处理，但FPGA在性能与适应性之间取得平衡，支持针对任务需求的机载部署。本综述系统分析了68项将机器学习模型部署于FPGA的遥感应用实验，提出了两种不同的分类体系，分别涵盖高效模型架构与FPGA实现策略。为确保透明性与可复现性，研究遵循PRISMA 2020指南，并将所有数据与代码公开于https://github.com/CedricLeon/Survey_RS-ML-FPGA。
原文摘要 · Abstract (English)
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.
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