NASA Uses AI to Analyze Satellite Images in Orbit

The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of GPUs aren’t the only way LLMs might prove useful in space.

NASA’s Jet Propulsion Laboratory recently sent Google’s Gemma 3 to space, achieving the first in-orbit demonstration of a vision-language model analyzing imagery from a satellite’s own sensor.

The system, known as NAVI-Orbital, used Gemma 3 to analyze images captured by a YAM-9 satellite built by Loft Orbital. Juan M. Delfa, technical group lead at NASA, said that though the goal in this case was image analysis, the project’s success implies a fundamentally new way researchers on the ground can interact with spacecraft.

“This is a major shift,” said Delfa. “Now, a scientist can write a prompt, upload it to the spacecraft, and that will be taken into account by the system. It’s different from previous paradigms, where researchers have to write very structured commands that require an operations team and process.”

Google Gemma 3 goes to space—no modifications required

At its core, NAVI-Orbital is an agentic software framework developed by Delfa and his co-authors, Taran Cyriac John, an AI researcher at NASA JPL, and Andrew W. Herson, a tech lead at Loft Orbital. It coordinates operations with a LangGraph-based conductor and deploys a compressed, 4-bit format of Google’s Gemma 3 4B, an open-weights LLM, to produce plain-text image descriptions.

NAVI-Orbital was 88 percent accurate when used to classify images in a benchmark dataset of 7,960 images. Notably, Gemma 3 classified the images without being trained or fine-tuned on this particular dataset or its categories; it’s the same base model you can download from Hugging Face and use on a laptop. The benchmark was conducted on the ground to validate the system before launch.

Once in orbit, NASA researchers performed two live capture tests with a camera on Loft’s YAM-9 satellite: one over Toulouse, France, and a second over the coast of Argentina. In addition to generating a text description of each image, NASA prompted Gemma 3 with a set of scripted questions about the images, such as whether they contain commercial or residential areas, or show natural features.

The image analysis also took place on-board YAM-9, which carries a compute cluster of several radiation-hardened processors (FPGAs, CPUs, and GPUs) to serve multiple customer payloads simultaneously. The satellite is powered by solar panels, which provide on-board systems with between 150 and 500 watts, depending on the position of the satellite.

For the live capture experiment, Gemma 3 ran on Nvidia’s Jetson Orin AGX, a small compute module frequently used for robotics and AI tasks. The 4-bit, 4-billion-parameter model requires only 8GB of memory, which makes it possible to run on a lower-power device such as the Orin AGX. “It conveys the message of how lightweight it is. You…