NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation
This paper presents NAVI-Orbital, a software system on a LEO spacecraft that achieved the first in-orbit demonstration of a vision-language model performing autonomous multi-modal inference entirely onboard on April 16, 2026. Using Gemma 3 and LangGraph, it classifies scenes, generates descriptions, and responds to operator dialogue. Ground benchmark accuracy 88.16%, and it successfully processed uncorrected YAM-9 imagery onboard, demonstrating feasibility of semantic compression to reduce downlink bandwidth.
[2606.18271] NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation
[Submitted on 5 Jun 2026]
Title:NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation
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Abstract:As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence. This paper presents NAVI-Orbital, a software system deployed on a Low Earth Orbit (LEO) spacecraft. On April 16, 2026, NAVI-Orbital achieved what is, to the authors' knowledge, the first in-orbit demonstration of a vision-language model performing autonomous multi-modal inference entirely onboard. NAVI-Orbital uses a local vision-language model (Gemma 3) to classify each captured scene, produce a text description of its content and the relationships between its features, and respond to operator follow-up via natural-language dialogue. The system is re-tasked through plain-English prompts in place of conventional command sequences, and is orchestrated by a graph-based state machine (LangGraph) coordinating dedicated agents for detection and dialogue. Results across ground benchmarking (88.16% accuracy on the 7,960-image curated AID benchmark), Flatsat validation, and live in-orbit captures of newly acquired, previously unseen Earth imagery (including uncorrected YAM-9 imagery, processed onboard with hardware-accelerated GPU inference and no fine-tuning for the flight instrument) demonstrate the feasibility of running foundation models on satellite-class edge computers to invert the conventional acquire-then-downlink-everything bandwidth profile through semantic compression of Earth observations in-orbit.
Comments: 17 pages, 47 figures
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.18271 [cs.AI]
(or arXiv:2606.18271v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.18271
arXiv-issued DOI via DataCite
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From: Juan Manuel Delfa Victoria [view email] [v1] Fri, 5 Jun 2026 06:46:54 UTC (34,294 KB)
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