  Recent advancements in satellite technologies and the declining cost of
access to space have led to the emergence of large satellite constellations in
Low Earth Orbit. However, these constellations often rely on bent-pipe
architecture, resulting in high communication costs. Existing onboard inference
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 satellite design
specifically tailored for Earth Observation tasks, enabling diverse computing
paradigms. In this work, we present a case study of a satellite-ground
collaborative inference system deployed in the Tiansuan constellation,
demonstrating a remarkable 50\% accuracy improvement and a substantial 90\%
data reduction. Our work sheds light on in-orbit energy, where in-orbit
computing accounts for 17\% of the total onboard energy consumption. Our
approach represents a significant advancement of cloud-native satellite, aiming
to enhance the accuracy of in-orbit computing while simultaneously reducing
communication cost.
