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AI on a satellite analyzes sensor or spacecraft data aboard the spacecraft, before or during transmission to Earth. That onboard processing can help select useful observations, identify events, or guide a follow-up action. It does not replace ground stations: the satellite still needs to send data and telemetry, and ground systems continue to receive, process, and deliver them.
What “AI on a satellite” means
Onboard processing is computation performed on a spacecraft after it collects data and before or during transmission to Earth. When that computation takes place close to where the data is generated, it is a form of edge computing. The terms describe different things: “edge” identifies the processing location, while AI and machine learning describe methods used to interpret data or make decisions.
An onboard system might classify or segment an image, compress data, score an observation for priority, or flag a target. It can be a specialized model or other decision-making software; it does not have to be a general-purpose chatbot. The satellite may use the result to decide what data to send or, if the mission is designed for it, what to observe next.
How data moves from a sensor to users
- A payload collects measurements. An Earth-observation instrument, for example, records imagery or other sensor data aboard the satellite.
- Onboard software analyzes or prepares some data. It may classify an image, identify a target, compress a file, or prioritize an observation for transmission.
- The spacecraft may act on the result. If its mission design and software permit, an onboard result can prompt the instrument to point elsewhere or take another observation.
- The satellite sends data during a ground-station contact. A transmission can include selected imagery, derived results, and telemetry—information about the spacecraft and its systems.
- Ground systems continue processing and delivery. They receive and route data, support mission operations, and make information available to operators or researchers. Some ground architectures move mission-specific processing from equipment at each station into cloud systems.
A ground station is communications infrastructure that exchanges data with a spacecraft during a contact. It is not the satellite’s onboard computer. Ground data systems are the broader services and equipment that collect, process, and deliver information after or as it arrives. NASA’s DAPHNE describes a cloud-based approach to ground-side mission processing, while its ASTRA technology demonstrator illustrates telemetry passing through commercial ground stations to mission operations.
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What onboard AI can do
Prioritize data for transmission
Spacecraft have limited opportunities and capacity to send data to Earth. Onboard analysis can help select observations likely to be useful, instead of treating every raw image as equally important. The result may be less data to transmit, but the system still needs to send selected data and relevant telemetry.
React to an observation
An onboard system can potentially respond while a target remains in view, rather than waiting for an image to travel to Earth, be reviewed, and generate a command back to the spacecraft. NASA and JPL’s Dynamic Targeting flight test demonstrated an automated loop: the spacecraft used a look-ahead sensor and onboard algorithms to identify clouds to avoid and targets of interest, then determined where to point an instrument. NASA reported that the process took less than 90 seconds. That is a result from this specific test, not a general measure of satellite AI speed.
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Support spacecraft autonomy
AI and other onboard software can also help interpret spacecraft-health data or support autonomous operations. NASA’s ASTRA demonstrator uses onboard processors to monitor and manage satellite systems, including electrical power. Such capabilities do not imply that every spacecraft decision is autonomous: what the vehicle may do without ground authorization depends on the mission and its operational rules.
Examples from NASA demonstrations
Dynamic Targeting: selecting and revisiting observations
In July 2025, NASA reported a commercial satellite flight test in which onboard analysis identified clouds to avoid and targets of interest, then selected where to point an instrument without human involvement. The spacecraft was reported to be moving at nearly 17,000 mph (7.5 kilometers per second) in low Earth orbit. That figure describes the test spacecraft’s reported orbital speed, not AI performance. NASA’s Dynamic Targeting account explains the demonstration.
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NASA reported in 2026 that researchers uploaded and demonstrated a compressed version of the Prithvi Geospatial model aboard South Australia’s Kanyini satellite and the IMAGIN-e payload on the International Space Station. They tested flood and cloud detection across the two platforms and computing environments. NASA says active satellites may have limited bandwidth for large software updates, one reason in-orbit models tend to be compact and task-specific. This was a demonstration of a compressed model, not evidence that satellites generally run large models without adaptation. NASA’s Prithvi in-orbit account describes the work.
Companion processors: adding compute for selected tasks
Some systems use a companion processor to analyze data separately from a satellite’s main avionics. NASA Spinoff describes Ubotica’s CogniSAT platforms as processors for in-orbit data analysis. NASA and JPL collaborated with Ubotica on International Space Station tests of image-analysis models and processor operation in a radiation environment. The account describes hardware and software measures intended to detect or resist radiation effects; it does not mean radiation risk disappears. NASA Spinoff’s account covers the technology.
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Why not process everything on Earth?
Onboard analysis can shorten the time between collecting an observation and acting on it, and it can help avoid transmitting every raw measurement. Those benefits matter when an event is brief, a target will soon leave view, or downlink capacity is constrained. In the Dynamic Targeting test, for example, the spacecraft could analyze imagery and determine an instrument pointing action without waiting for a ground-based review loop.
Processing on Earth still has advantages: ground systems can provide mission operations, data delivery, and additional processing after a transmission. In practice, many architectures divide work between spacecraft and ground rather than choosing one location for everything.
Constraints that shape the onboard system
- Power, mass, cooling, and compute: a spacecraft has finite resources, and computation competes with instruments and other systems for them. NASA’s 2024 SMARTIE technology highlight reports over 300 gigaflops of compute and 15 TOPS of AI performance for one folded-flex computer-tile module. Those are specifications for that particular technology, not typical figures for all satellites. NASA’s SMARTIE account describes the module.
- Radiation and fault handling: radiation can cause hardware errors or data corruption. Flight systems may need radiation-tolerant components, software checks, and ways to detect or contain faults; mitigation approaches described in NASA’s Ubotica account were part of a specific test.
- Model size and updates: limited communications bandwidth and mission risk can make large software updates difficult. Models therefore need to be suited to a defined task, and changes must fit the mission’s update and validation process.
- Authority to act: detecting an event does not automatically grant a system permission to change spacecraft behavior. The mission defines which actions can be taken onboard and which require ground involvement.
How to compare satellite-computing architectures
When assessing an implementation, identify where each stage runs and what it is allowed to do. A system may use a payload computer, a separate companion processor, spacecraft avionics, a ground station, a cloud service, or several of these together.
| Question | What to establish |
|---|---|
| Where is the processing? | Distinguish payload hardware, a companion processor, spacecraft avionics, station equipment, and cloud services. |
| How quickly is a result available? | Measure the time from observation to useful result or action, and identify which portions occur onboard and on the ground. |
| What data must be transmitted? | Determine whether the system filters, compresses, or prioritizes data, and what raw data and telemetry still need to reach Earth. |
| What are the resource limits? | Check compute and power budgets alongside instrument and spacecraft requirements. |
| How are errors handled? | Establish radiation resilience, fault detection, recovery behavior, and operator visibility. |
| How is the model maintained? | Check the model’s task and size, how it is validated, and how updates can be delivered to an operating spacecraft. |
| What can the spacecraft do autonomously? | Clarify which decisions can be taken onboard, which require authorization, and how operators monitor outcomes. |
| What does the ground service provide? | For ground-side services, compare contact coverage, data handoff, processing location, and integration with mission operations. |
What the examples do—and do not—show
NASA’s demonstrations establish that onboard analysis can support tasks such as image selection, event detection, spacecraft-health monitoring, and instrument retargeting. They do not establish that every satellite carries AI, that every onboard result triggers an autonomous action, or that onboard processing makes ground communications unnecessary. Performance figures apply to the named tests or hardware only.
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