NASA’s new AI satellites could revolutionize disaster response

Satellite-based disaster monitoring has been a slow and tedious process for decades. The process consists of capturing images, transmitting them back to Earth, and relying on human analysts to interpret the data. This often led to first responders receiving critical information, often too late to act effectively. 

But AI is now revolutionizing satellite operations in space, aiding real-time image processing and autonomous decision-making. NASA’s latest space venture focuses on AI-powered autonomous satellites that can operate without human oversight. In collaboration with Ireland-based satellite intelligence startup Ubotica, NASA’s Jet Propulsion Laboratory (JPL) has developed Dynamic Targeting, an AI-driven system that allows satellites to process image data onboard, potentially enhancing disaster response. 

Dynamic Targeting was recently tested in real-world scenarios, including the Palisades Fire in Los Angeles and the Valencia floods. The AI system was integrated in CogniSAT-6 satellite, a 6-unit cubesat developed by Ubotica and NASA JPL, and autonomously processed data onboard and transmitted insights to Earth within minutes. At the core of CogniSAT-6 lies Live Earth Intelligence (LEI)—Ubotica’s onboard processing platform that integrates AI agents into Earth observation satellites. Paired with SPACE:AI, an end-to-end vision processing framework, the architecture transforms conventional satellites into always-connected observers, enabling rapid decision-making.

“With LEI, we can quickly deploy and run AI models from third parties directly in orbit. By leveraging Inter-Satellite Links (ISL), these insights reach the right people faster, ensuring critical information gets where it’s needed, exactly when it’s needed,” Fintan Buckley, CEO of Ubotica, told Fast Company. “We’re at an inflection point. Satellites will no longer just observe; they’ll analyse, interpret, and respond in real time.” 

Dynamic Targeting: The Future of AI-Powered Earth Observation

CogniSAT-6’s Dynamic Targeting system can analyze lookahead images in as little as 50 seconds. Buckley added that if one satellite can’t capture the image due to cloud cover, the AI system alerts others to try on their next pass, removing the need for operators to retask satellites manually. In mixed constellations, the system can even switch to radar imaging (SAR) when clouds are an issue, ensuring data is still collected.

“With Dynamic Targeting, the satellite first takes a quick, low-resolution ‘look-ahead’ image, and onboard AI analyzes it for cloud cover. If the target area is clear, the satellite locks on and captures a high-resolution image. If not, it discards the request, conserving bandwidth and storage,” explained Buckley.

During the Palisades Fire in Los Angeles, identifying smoke plumes through autonomous imaging allowed emergency teams to monitor the fire’s spread effectively. Likewise, during the Valencia floods, the AI swiftly estimated that 21% of the observed area near Valencia was flooded, and sent down accurate flood data immediately to Earth.

“AI-powered satellites can analyze a scene and deliver insights to the ground within minutes, making Earth observation viable in situations where other imaging methods fall short,” said Buckley. “These satellites will soon be integrated with insights from other sources to create an accurate, up to date, view of the situation on the ground to support the responders to manage the situation.” 

The partnership between Ubotica and NASA’s Jet Propulsion Laboratory (JPL) began in 2022, when they collaborated to test AI-driven image processing aboard the International Space Station (ISS). Under a $632,000 contract with NASA’s Jet Propulsion Lab in California, Ubotica is currently preparing for the first live in-orbit test of Dynamic Targeting in early 2025 through CogniSAT-6. 

U.S. vs. China: The Geopolitical Race for Space Intelligence

While NASA and Ubotica are pushing the boundaries of satellite technology, they face competition from China. The country has been aggressively deploying its own AI-powered Earth observation satellites, Tiantuo and Zhuhai. Operated by the China Aerospace Science and Technology Corporation (CASC) and commercial partner Zhuhai Orbita, has already incorporated AI-driven image processing similar to NASA JPL’s initiatives.

The geopolitical implications of this race are profound, as autonomous satellites can also provide strategic intelligence on environmental changes, maritime security, and even military movements.

NASA JPL and Ubotica, however, are actively collaborating with defense agencies across the U.S. and Europe, to enhance Maritime Domain Awareness. Their AI-powered satellites are playing a crucial role in securing maritime assets such as underwater cables, offshore wind farms, and detecting suspicious vessel activity. 

“It is important to safeguard the vast network of underwater high-speed communication cables, as they often subject to accidental or deliberate damage,” Buckley added. “The key is to identify and warn off vessels before any damage occurs, and if an incident happens, track and hold the offending vessel accountable.” However, this leap in technology also raises critical questions about the reliability of AI decision-making in life-or-death situations.

Can AI be Trusted Without Human Oversight?

Traditionally, Earth observation has relied on human judgment to verify and interpret satellite data. AI-powered autonomous systems could misclassify minor weather shifts as emergencies or overlook critical events due to biased training data. Despite these concerns, Buckley claims that AI autonomy is inevitable. 

“Human oversight will eventually become obsolete,” Buckley told Fast Company. “But like almost every other disruptive technology, it will take much longer than anticipated for this to happen.” 

To mitigate AI errors, Ubotica’s Dynamic Targeting system integrates multiple safeguards through its Live Earth Intelligence (LEI) framework. “A built-in Neural Network Supervisor constantly monitors AI outputs, discarding insights that fall outside trained parameters. The system also cross-validates insights by incorporating data from multiple sources rather than relying solely on satellite imagery,” he explained. The system ensures continuous learning and improvement by actively selecting images to enhance future model performance.

NASA’s bet on AI is a bold leap into uncharted territory that could redefine how we monitor our planet and respond to crises. As Buckley explained, AI’s role isn’t just about analyzing satellite imagery; it’s about coordinating real-time responses. “With Dynamic Targeting, we can command other satellites in a constellation to provide real-time updates as a fire develops. Could this capability extend to drones responding to wildfires? Absolutely.”

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