Edge Intelligence for Satellite-based Earth Observation: Scheduling Image Acquisition and Processing
It addresses the problem of time-critical data processing for satellite-based Earth Observation, with applications like maritime surveillance, though it is incremental as it builds on existing edge computing and scheduling techniques.
This work tackles the challenge of processing massive Earth Observation imagery in real-time by proposing an energy-aware framework for scheduling image acquisition and processing on a satellite constellation with edge computing, showing that turbulence-aware scheduling improves target observation quality and quantity while cooperative edge processing reduces power consumption compared to traditional methods.
Modern Earth Observation (EO) missions generate massive volumes of imagery that challenge existing downlink and ground-processing capabilities, particularly for time-critical applications. This work investigates how a low Earth orbit (LEO) satellite constellation equipped with heterogeneous edge computing resources can enable real-time semantic processing of data acquired by EO satellites. We introduce an energy-aware framework that optimizes the use of resources accounting for data acquisition, computing, and communication constraints. Although we focus on maritime surveillance, the formulation is task-agnostic and accommodates a broad class of semantic and goal-oriented inference problems. Specifically, we formulate two coupled optimization problems: (i) observation scheduling, which selects image acquisition opportunities while accounting for turbulence-induced image degradation and energy budget, and (ii) processing scheduling, which allocates semantic workloads across onboard and ground processors. We evaluate these mechanisms for the task of detection and localization of vessels, for which we quantify the benefits of turbulence-aware observation scheduling for preserving image quality and experimentally characterize the execution-time distribution of YOLOv8 on different computing platforms. Results demonstrate that task- and turbulence-aware observation scheduling can significantly improve the quality and quantity of observed targets. Furthermore, cooperative edge processing within the constellation substantially reduces power consumption compared to traditional downlink-centric architectures. These findings highlight the potential of distributed edge intelligence to enhance the responsiveness and autonomy of future satellite-based EO systems.