Real-world applications such as on-orbit disaster assessment, precision agriculture, and maritime surveillance demand immediate semantic understanding of satellite imagery. Unfortunately, the lack of efficient, high-quality annotation for large-scale satellite imagery limits the responsiveness of low Earth orbit (LEO) constellations in dynamic observation scenarios. We propose SatCLA, a lightweight framework for collaborative LLM-driven annotation of LEO satellite imagery under resource-constrained edge environments. SatCLA enables lightweight large language models (LLMs) to perform zero-shot semantic labeling onboard by retrieving relevant visual-text features and generating structured prompts to guide annotation. However, challenges such as label inconsistency, LLM hallucination, and limited semantic generalization arise under constrained compute and data settings. To address these, SatCLA introduces three key designs: (1) a multimodal retrieval pipeline for context-aware prompting, (2) a structured prompt template to guide task-specific semantic constraints, and (3) an inter-satellite re-annotation mechanism that allows peer satellites to collaboratively verify and refine low-confidence outputs. We conduct experiments within an emulated heterogeneous LEO satellite-cluster testbed and on multiple remote sensing benchmarks, demonstrating that SatCLA achieves significant gains in annotation accuracy and consistency compared to baseline methods, while maintaining low latency and high throughput.

With the advancement of satellite computing capabilities, the diversity of satellite communication services imposes varied quality of service (QoS) requirements. Limited satellite resources necessitate remote deployment and updates of running services for QoS testing, increasing testing difficulty. Existing testing tools are limited in functionality or reliant on specific infrastructures, failing to meet the QoS testing needs of edge cloud services in mobile satellite scenarios. In this paper, we present SECTest, an integrated testing platform for QoS in satellite edge clouds. More precisely, SECTest can integrate changes in satellite network topology, create and manage satellite edge cloud cluster testing environments on heterogeneous edge devices, customize experiments for users, support deployment and scaling of various integrated testing tools, provide test data persistence function to manage data life cycle and store data hierarchically, and publish and visualize test results. We have built a real satellite edge cloud cluster based on Kubernetes, integrating both physical and virtual machines, and deploying a variety of integrated testing tools using containerization technology. Currently, we have evaluated the quality of service in terms of processing latency, packet drop rate, throughput, and average response time for object detection microservice applications, web microservice applications, and data transfer tasks. To demonstrate SECTest's scalability in testing network communication protocols, we evaluated the performance of HTTP and gRPC in microservice communication within the cluster. Our experimental results validate SECTest's ability to test key service quality metrics in a real satellite edge cloud cluster.

The diversification of satellite communication services imposes varied requirements on network service quality, making quality of service (QoS) testing for microservices running on satellites more complex. Existing testing tools have limitations, potentially offering only single-functionality testing, thus failing to meet the requirements of QoS testing for edge cloud services in mobile satellite scenarios. In this paper, we propose a framework for integrating quality of service testing in satellite edge clouds. More precisely, the framework can integrate changes in satellite network topology, create and manage satellite edge cloud cluster testing environments on heterogeneous edge devices, customize experiments for users, support deployment and scaling of various integrated testing tools, and publish and visualize test results. Our experimental results validate the framework’s ability to test key service quality metrics in a satellite edge cloud cluster.