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Updated: Jan 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Optimizing deep learning models for on-orbit deployment through neural architecture search.

Roberto Del Prete^1,2, Parampuneet Kaur Thind^3,4, Andrea Mazzeo⁵

¹Φ-lab, European Space Agency (ESA), ESRIN, Via Galileo Galilei, Frascati, 00044, Italy. roberto.delprete@esa.int.

Scientific Reports

|October 30, 2025

Summary

This summary is machine-generated.

This study introduces a Neural Architecture Search (NAS) framework for optimizing AI models on CubeSats. The NAS approach achieves efficient, real-time onboard processing for spaceborne edge computing, outperforming existing methods.

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Area of Science:

Spaceborne edge computing
Artificial Intelligence (AI) in satellites
Neural Architecture Search (NAS)

Background:

CubeSats face significant energy and memory constraints for onboard AI processing.
Lightweight AI models are crucial for autonomous data handling in space.
Existing model compression techniques may not be optimal for hardware-specific constraints.

Purpose of the Study:

To develop and evaluate an evolutionary-based NAS framework for optimizing AI models on resource-constrained CubeSats.
To enable efficient, hardware-aware model compression for onboard processing.
To balance accuracy, size, and latency for real-time inference in orbit.

Main Methods:

Designed an evolutionary-based NAS framework incorporating hardware awareness.
Jointly optimized network architecture and deployment for CubeSat-class hardware (NVIDIA Jetson AGX, Intel Myriad X).
Evaluated the framework on burned-area segmentation and classification tasks.

Main Results:

Achieved models with <1MB memory footprint, enabling real-time, high-resolution inference.
Models demonstrated 3x lower latency than handcrafted baselines while maintaining competitive mIoU.
Attained a Matthew Correlation Coefficient (MCC) of 0.974 on classification, with a 47x speedup over EfficientNet-lite0.

Conclusions:

The NAS framework effectively optimizes AI models for spaceborne edge computing.
The approach offers scalable solutions from resource-constrained devices to datacenter accelerators.
This work supports the development of next-generation on-orbit computing architectures.