Satellite Edge Computing for Real-Time and Very-High Resolution Earth Observation

Abstract

In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for realtime disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of 12× and 2× when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.

APOLLO: A Proximity-Oriented Low-Level Orchestration Algorithm for Performance Optimization in Mist Computing

“…This model, deployed on Arm Cortex-M3 microcontrollers onboard satellite payloads, demonstrates significant acceleration compared to conventional convolution models while maintaining high-quality image processing. In a related context, another investigation by Zhang et al [10] addresses the challenges of handling large volumes of Earth observation imagery data transmitted by Low Earth Orbit (LEO) satellites. The study proposes a satellite mobile edge computing (SMEC) framework to optimize image distribution and compression parameters, thereby minimizing energy consumption while maintaining real-time processing capabilities.…”

Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

“…The objective of next-generation studies [15] is then to optimize Earth Observation (EO) data processing in order to deliver EO products to the end user with very low latency using a combination of advancements in onboard processing. As a result, in the field of the EO, the demand for real-time decisionmaking capabilities jointly with the need to optimize data acquisition and transmission has led to a significant interest in onboard intelligence, particularly in scenarios such as the early detection of disasters, extreme events, maritime situation awareness, and automatic target recognition, where swift and informed decisions can mitigate potential risks and minimize impacts and damages [16].…”

“…However, as pointed out in [16], the research on Edge Computing (EC) to reduce the amount of data transmission and energy consumption in EO applications is still in its infancy. As a response to this demand, preliminary research activities have been directed toward the exploration of onboard intelligence for EO applications based on optical sensors.…”