Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning

Abstract

There is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. In particular, we design a self-supervised lightweight backbone that provides features to efficient task-specific heads. The latter can be developed independently and with reduced data labeling requirements thanks to the frozen backbone. Experiments on three sample tasks of cloud segmentation, flood detection, and marine debris classification on a 7-W embedded system show competitive results with inference quality close to high-complexity state-of-the-art models and high throughput in excess of 8 Megapixel/s.

Keywords

• Multitask learning
• onboard AI
• self-supervised learning (SSL)