Satellite image deep learning AutoML for Spaceborne AI
In this episode I caught up with Roberto del Prete to learn about his work on AutoML for in-orbit model deployment, and how it enables satellites to run highly efficient AI models under severe power and hardware constraints. Roberto explains why traditional computer-vision architectures—optimised for ImageNet or COCO—are a poor fit for narrow, mission-specific tasks like wildfire or vessel detection, and why models must be co-designed with the actual edge devices flying in space. He describes PyNAS, his neural architecture search framework, in which a genetic algorithm drives the optimisation process, evolving compact, hardware-aware neural networks and profiling them directly on representative onboard processors such as Intel Myriad and NVIDIA Jetson. We discuss the multiobjective challenge of balancing accuracy and latency, the domain gap between training data and new sensor imagery, and how lightweight models make post-launch fine-tuning and updates far more practical. Roberto also outlines the rapidly changing ecosystem of spaceborne AI hardware and why efficient optimisation will remain central to future AI-enabled satellite constellations.
* 🖥️ PyNAS on Github
* 📖 Nature paper
* 📺 Video of this conversation on YouTube
Bio
Roberto is an Internal Research Fellow at ESA Φ-lab specialising in deep learning and edge computing for remote sensing. He focuses on improving time-critical decision-making through advanced AI solutions for space missions and Earth monitoring. He holds a Ph.D. at the University of Naples Federico II, where he also earned his Master’s and Bachelor’s degrees in Aerospace Engineering. His notable work includes the development of “FederNet,” a terrain relative navigation system. Del Prete’s professional experience includes roles as a Visiting Researcher at the European Space Agency’s Φ-Lab and SmartSat CRC in Australia. He has contributed to key projects like Kanyini Mission, and developed AI algorithms for real-time maritime monitoring and thermal anomaly detection. He co-developed the award-winning P³ANDA project, a compact AI-powered imaging system, earning the 2024 Telespazio Technology Contest prototype prize. Co-author of more than 30 scientific publications, Del Prete is dedicated to leveraging advanced technologies to address global challenges in remote sensing and AI.
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