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

TL;DR

This systematic review explores how FPGA technology enables real-time machine learning applications in Earth Observation, highlighting recent experiments, methodologies, and implementation strategies for onboard data processing.

Contribution

It introduces two taxonomies for classifying FPGA-based ML models and implementation strategies, providing a comprehensive framework for future research in this area.

Findings

68 experiments analyzed on FPGA-based ML for Remote Sensing

Two taxonomies developed for model architectures and implementation strategies

All data and code shared for transparency and reproducibility

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 https://github.com/CedricLeon/Survey_RS-ML-FPGA.