Flight Demonstration of On-Orbit Model Adaptation on a Nanosatellite

Zaher Joukhadar, Miguel Ortiz del Castillo, Jonathan Morgan, Lachlan Cowley, Robert Mearns, Simon Barraclough, Krista A. Ehinger, Benjamin I. P. Rubinstein, Richard Sinnott, Michele Trenti, James Bailey

Keywords: On-orbit model adaptation, In-flight machine learning, Telemetry-supervised learning, Onboard fine-tuning

Abstract: AI models launched on spacecraft are often frozen at deployment, yet sensing conditions and operational demands in orbit change. In-orbit updates are hard because the standard loop (e.g., downlinking new in-orbit data such as full-resolution images, labeling them on Earth, retraining models on Earth, and uplinking new weights) is constrained by bandwidth, power, thermal limits, and short operations windows. We show that meaningful in-mission adaptation is possible under these constraints and introduce Telemetry-First In-orbit Fine-Tuning (TFiT), an operations-first framework for fine-tuning AI models in space. Telemetry denotes compact kilobyte-scale non-image records downlinked from the spacecraft, including logits, timestamps, geolocation, and spacecraft status. TFiT avoids routine image downlink: labels are produced on Earth by fusing telemetry with Earth-based context sources, while thumbnails are downlinked for human review only when label evidence is low-confidence or conflicting. Crucially, in-orbit data remains onboard and only approved labels are uplinked for bounded on-orbit updates. We demonstrate TFiT on a flight-operated 6U nanosatellite by executing an in-orbit update of an onboard cloud-detection model (OrbitBaseline to OrbitAdapted) and evaluating pre/post-update behavior with a same-image audit protocol. Under the recovered same-image audit, OrbitAdapted improves ROC AUC by +0.510 and F1 score by +0.871 over the pre-update baseline. These results demonstrate in-mission adaptation without routine full image downlink, widening the practical scope of AI in space.

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Submission Number: 7