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.

翻译：现代对地观测任务产生大量图像数据，给现有的下行链路和地面处理能力带来挑战，尤其是在时效性要求高的应用场景中。本研究探讨了配备异构边缘计算资源的低地球轨道卫星星座如何实现对地观测卫星所获取数据的实时语义处理。我们提出了一种能量感知框架，该框架在考虑数据采集、计算和通信约束的前提下优化资源利用。尽管本文以海上监视为研究对象，但所提出的公式与具体任务无关，适用于广泛的语义和目标导向推理问题。具体而言，我们形式化了两个耦合的优化问题：(i) 观测调度，在考虑湍流引起的图像退化及能量预算的同时，选择图像采集机会；(ii) 处理调度，将语义工作负载分配至星载和地面处理器。我们针对船只检测与定位任务评估了这些机制，量化了顾及湍流影响的观测调度在保持图像质量方面的优势，并通过实验刻画了YOLOv8在不同计算平台上的执行时间分布。结果表明，融合任务与湍流感知的观测调度能显著提升观测目标的质量和数量。此外，与传统的以下行链路为中心的架构相比，星座内的协同边缘处理大幅降低了功耗。这些发现凸显了分布式边缘智能在增强未来卫星对地观测系统响应能力与自主性方面的潜力。