The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites

Recent advancements in satellite technolo-gies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constel-lations often rely on bent-pipe architecture,resulting in high communication costs.Existing onboard infer-ence architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications.To address these challenges,we propose a cloud-native-based satel-lite design specifically tailored for Earth Observation tasks,enabling diverse computing paradigms.In this work,we present a case study of a satellite-ground col-laborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50％accu-racy improvement and a substantial 90％data reduc-tion.Our work sheds light on in-orbit energy,where in-orbit computing accounts for 17％of the total on-board energy consumption.Our approach represents a significant advancement of cloud-native satellite,aim-ing to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.