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Ling Xin

China has unveiled its latest ambitious space-based computing network designed to process satellite data in orbit using AI before sending the results to Earth.
Xingshu, or “Star Hub”, will eventually comprise around 1,000 satellites, according to Chi Nan, an optical communications professor at Fudan University.
The network is being jointly developed by Fudan and Shanghai Xingshu Tiansuan Space Technology Co.
It would offer on-demand, time-based computing services in orbit, allowing governments, researchers and companies in sectors such as energy, shipping and finance to rent computing power by the hour, Chi said at the World Artificial Intelligence (AI) Conference in Shanghai on Saturday.
Xingshu will also combine satellite observations with AI to provide weather forecasts, disaster warnings, port and shipping monitoring, power grid inspections and remote-sensing services for emergency responders, meteorological agencies and natural resource authorities.
The project is the latest in a series of Chinese space-computing initiatives, following the 300-satellite Tiansuan constellation led by the Beijing University of Posts and Telecommunications, and state-backed Zhejiang Lab’s 1,000-satellite Three-Body Computing Constellation.
Tiansuan has launched eight satellites so far, while Three-Body deployed its first dozen satellites last year.
The idea behind space-based computing is that the data is born in space, so it should also be processed there.
Instead of sending every image or measurement back to Earth, satellites analyse the data in orbit and transmit only the results, enabling real-time weather forecasts and disaster warnings while freeing up downlink capacity.


According to Chi, Xingshu’s basic computing architecture combines a central computing satellite – or “space brain” – with weather and Earth observation satellites. Linked by laser communications, the satellites collect data, run AI models and complete their analysis in orbit before sending the results to Earth.
Chi said the project would be rolled out in three phases, beginning with an engineering demonstration involving two computing satellites and 12 AI-enabled sensing satellites.
It will then expand to 50 computing and 100 AI-enabled sensing satellites, before reaching its final goal of a 1,000-satellite constellation.
US tech tycoon Elon Musk’s SpaceX has proposed a different approach to space-based computing. In January, it asked US regulators for permission to deploy up to 1 million satellites as orbital AI data centres, arguing that space offered abundant solar power and avoided the heavy water use needed to cool data centres on Earth.
But scientists in China have argued that the country does not need to follow Musk’s approach, because electricity is not a major constraint on its AI data centres.
Instead, they say, China should focus on processing satellite-generated data in orbit and transmitting only useful information back to Earth – the strategy pursued by the Xingshu, Tiansuan and Three-Body constellations.
Western researchers have also questioned Musk’s proposal, citing the cost and technical complexity of powering and cooling hyperscale computers in orbit, as well as the environmental impact of manufacturing, launching and eventually de-orbiting such a vast fleet.


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News

China

SpaceX views orbital computing as a solution to Earth’s constraints, but China hopes to process data at the source to optimise efficiency


SpaceX views orbital computing as a solution to Earth’s constraints, but China hopes to process data at the source to optimise efficiency.


Shanghai

Elon Musk

Fudan University

SpaceX

China targets new space-based computing network to process data born in orbit

.css-1c6uqr6{color:inherit;font-weight:inherit;font-size:inherit;font-family:inherit;line-height:inherit;overflow-wrap:break-word;}SpaceX views orbital computing as a solution to Earth’s constraints, but China hopes to process data at the source to optimise efficiency

SpaceX views orbital computing as a solution to Earth’s constraints, but China hopes to process data at the source to optimise efficiency