Earth observation satellites face a persistent bottleneck: they gather more data than they can transmit to the ground. A new paper from arXiv proposes a generic on-board AI processing chain that treats the satellite as a data-reduction stage, downlinking compact products such as masks rather than raw imagery.
The chain is designed to cover the full lifecycle of an AI application, from development through embedded deployment to mission-level orchestration. This suggests a framework for making on-board intelligence practical across different satellite missions, rather than a one-off custom solution.
The abstract is brief and does not include experimental results or comparisons with other approaches. Its contribution is the architecture itself, aimed at closing the gap between AI application development and operational deployment in space.