First Satellite to Find Objects Autonomously in Orbit – a Breakthrough for Space AI
An Earth observation satellite has for the first time autonomously identified areas of interest using a vision-language model in orbit, potentially revolutionizing satellite data analysis.
In April, a milestone was achieved in space technology: Loft Orbital's Yam-9 satellite became the first Earth observation spacecraft to find what it was looking for on its own, without human analysts on the ground. It marks the first reported use of a vision-language model (VLM) in orbit, offering a glimpse of how AI could fundamentally change the capabilities and value of space-based sensors.
Traditionally, satellites download large volumes of data for analysts on Earth, who use machine learning algorithms or their own eyes to interpret it. On Yam-9, a software package called NAVI-Orbital, built by NASA's Jet Propulsion Laboratory, harnessed Google DeepMind's Gemma 3 VLM to identify areas of interest in response to natural language queries. For example, researchers asked the model to classify sensor data where natural environments meet human development, or to identify infrastructure around railway hubs—and it succeeded.
The demonstration holds significance on two fronts. In the near term, it makes space sensors far more useful by performing initial data triage on orbit, reducing the flood of raw data analysts must process. Longer term, it serves as a proof point for deploying larger-scale AI infrastructure in space. “It opens the door to always-on, patrol layers in space,” Paul Lasserre, Loft's head of AI, told TechCrunch. “If you have a VLM, you can have logic—like ‘monitor this border for me, and let me know when something is suspicious,’ and interact back and forth with the satellites.”
Loft's spacecraft are designed as platforms for third-party customers. One recent deal involved building, launching, and operating six new satellites for EarthDaily, which will analyze and market the data collected onboard. Yam-9, launched in fall 2025 as a pathfinder for orbital AI projects, carries an Nvidia Jetson Orrin AGX GPU. Juan Delfa Victoria, a technical leader at NASA JPL's AI group, led the development of NAVI-Orbital, which streamlined the software package to minimize memory and library requirements.
Other companies are following suit. Planet Labs flies satellites with Jetson Orin processors; currently used for simpler object detection, but research on VLM and other AI applications is underway. Kepler Communications, which operates the largest group of GPUs in space, declined to disclose specific VLM deployments due to NDAs but noted “several undisclosed use cases of our compute environment” since January.
“Now that we've proven the concept, that's really the direction of travel,” Lasserre said. The goal is to expand the constellation to 50–100 satellites like Yam-9 to ensure real-time coverage of anywhere on Earth (Loft currently operates 12). Lessons from deploying smaller models will inform larger-scale compute infrastructure in space, especially in power and memory management. The technology could also enable new scientific tools. The idea for NAVI-Space began with JPL researcher Taran Cyriac John, who envisioned digital assistants for astronauts exploring the Moon or Mars. “We're thinking, okay, you have astronauts with pressurized suits, and you know they cannot be tapping on a keyboard,” Delfa Victoria said. “So, how about we provide an assistant, like in video games and in movies, where you see an AI which is interactive?” Just don't call it HAL 9000.
