FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review
This review provides a structured analysis for researchers and practitioners in Earth observation, but it is incremental as it synthesizes existing work rather than presenting new methods.
The paper systematically reviews 66 experiments that deploy machine learning models on FPGAs for remote sensing applications, introducing taxonomies for model architectures and implementation strategies to address the challenge of processing large Earth observation data volumes in real-time.
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 66 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.