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.

翻译：新型无人机技术与新航天时代正在变革地球观测任务与数据获取方式。众多小型平台产生海量数据，对传输带宽构成压力，并需要星载决策系统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在性能与任务特定需求的适应性之间取得平衡，从而支持星载部署。本综述系统分析了68项在FPGA上部署机器学习模型用于遥感应用的实验。我们提出两种分类体系，分别聚焦高效模型架构与FPGA实现策略。为确保透明度与可复现性，本研究遵循PRISMA 2020指南，所有数据与代码均公开于https://github.com/CedricLeon/Survey_RS-ML-FPGA。