Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The dynamic network topology and time-varying resource availability in satellite constellations further degrade the quality and stability of SEC services. To address these challenges, we propose a deep learning-based Collaborative Image Feature-extraction Task Optimization (CIFTO) framework. CIFTO dynamically distributes image processing workloads across multiple Low Earth Orbit (LEO) satellites, enabling continuous temporal updates for task allocation while significantly accelerating convergence and reducing computational overhead. By integrating temporal modeling and iterative optimization, CIFTO effectively mitigates the NP-hard nature of satellite task allocation. Furthermore, a lightweight satellite image processing model is designed to meet the strict constraints of on-orbit computation, achieving efficient image inference with minimal parameters. Extensive experimental evaluations demonstrate that the proposed framework ensures timely task completion, substantially lowers system-wide energy consumption, and enhances the adaptability and training efficiency of SEC services.
Earth observation (EO) constellations operated by organizations such as Planet, Google, and Amazon generate hundreds of terabytes of imagery every day. However, limited downlink bandwidth prevents immediate data transmission, forcing satellites to store large volumes of imagery onboard and often overwrite valuable data before it can be downlinked. Existing EO pipelines treat each capture independently and fail to exploit the substantial redundancy naturally present in EO constellations. In practice, two major redundancies dominate: (i) temporal stability, where consecutive images of the same area change minimally over time, and (ii) spatial overlap, where neighboring satellites capture largely identical ground regions. To address these inefficiencies, we present CoOrbit, a collaborative EO system that conserves onboard storage by retaining only changed and non-overlapping regions. CoOrbit combines lightweight onboard embedding differencing, TLE-based overlap inference, and adaptive reference embedding planning. We further extend CoOrbit with an application-driven design, allowing satellites to downlink only application-relevant tiles for even greater efficiency. Evaluations on satellite-grade GPUs and imagery demonstrate that CoOrbit achieves over 108.6× reduction in storage cost and 41.6× reduction in communication size compared to existing EO pipelines. Our evaluation also shows an additional order-of-magnitude reduction when the wildfire-driven design is applied.
Mega-constellations of Low Earth Orbit (LEO) satellites are enabling a new Space Cloud paradigm in which edge servers hosted on-board process tasks autonomously, reducing ground-segment latency for globally dispersed users. Realizing this vision requires Collaborative Edge Intelligence (CEI): distributed algorithms that coordinate Multi-access Edge Computing (MEC) server activation, task offloading, and routing across a time-varying orbital topology. Existing evaluation tools are inadequate—simulation platforms abstract away protocollevel behavior, while emulation testbeds lack orbital dynamics. This paper presents MeteorNet, an open-source, continuous-time emulation platform that integrates SGP4 orbital propagation, Mininet/ONOS network emulation, Docker-containerized MEC services, and MongoDB telemetry. Supporting eight orchestration strategies, including Fuzzy Logic and distributed Reinforcement Learning controllers, MeteorNet enables apples-to-apples CEI benchmarking under realistic orbital conditions. Experiments show that intelligent controllers halve the MEC activation cost at low load, while revealing an orbital-visibility bottleneck that limits task success at high load, regardless of the orchestration policy.
Modern Earth observation (EO) satellites carry increasingly advanced sensors that produce vast volumes of high-resolution, multispectral data, yet downlink capacity remains a critical bottleneck -- often causing significant latency or the loss of valuable observations within limited contact windows. We propose a"Summarize First, Download Later"paradigm that exploits recent advances in onboard edge computing and Vision-Language Models (VLMs). Rather than indiscriminately downlinking raw imagery, the system follows a three-phase interaction protocol: the satellite first transmits concise natural language summaries generated by a quantized onboard VLM; ground operators then issue targeted Visual Question Answering (VQA) queries to verify scene relevance (e.g., wildfires or maritime anomalies); and full-resolution images are downloaded only when critical information is confirmed. This transforms the downlink from passive bulk transfer into an active, semantics-aware dialogue. We implement and evaluate the system on a resource-constrained NVIDIA Jetson platform, and experiments on diverse remote sensing scenes show that the proposed strategy substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.
The rapid growth of Low Earth Orbit satellite constellations has created increasing demand for efficient and scalable downlink services. Ground Station as a Service (GSaaS) provides an on-demand access model for satellite operators, but commercial GSaaS providers must schedule limited ground-station bandwidth among multiple satellites with heterogeneous data demands, overlapping visibility windows, strict deadlines, and strategic bidding behaviors. This paper studies GSaaS resource scheduling from a ground-station-centric perspective, where the provider jointly determines task admission, ground-station assignment, bandwidth allocation, and payments. Under satellite orbital dynamics, bandwidth constraints, and downlink task deadlines, maximizing the provider's revenue is NP-hard. To address this challenge, we propose STAR-GS, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing. By integrating auction theory with schedulability analysis, STAR-GS incentivizes task owners to truthfully report their private valuations while ensuring that admitted tasks can be feasibly completed before their deadlines. Simulations using Ansys Systems Tool Kit (STK) show that STAR-GS consistently achieves higher revenue than heuristic baselines, obtains near-MILP performance with substantially lower runtime, and scales smoothly to workloads containing up to 900 tasks.
