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Earth intelligence is emerging as a transformative growth area, poised to generate nearly USD 20 billion in direct revenue for technology and service providers between 2025 and 2030, according to a recent report by Gartner.
The report expects that the annual revenue from Earth intelligence is expected to surpass USD 4.2 billion by 2030, up from nearly USD 3.8 billion in 2025. Gartner defines Earth intelligence as the application of AI to Earth observation data to generate industry-specific insights. It involves collecting satellite and sensor data, transforming it using advanced analytics and domain-specific AI models, and delivering actionable intelligence to businesses. "The future of Earth intelligence will be won by the vendors that move quickly to develop technologies that make sense of the oceans of raw data they collect," said Bill Ray, Distinguished VP Analyst at Gartner. "The value of Earth intelligence data is only now being realised. For example, vendors are leveraging satellites to pinpoint fallen trees blocking railroad tracks in a storm, monitor the temperature of every metal refinery to assess global production, count vehicles to analyse traffic patterns and consumer trends, and track sea cargo to evaluate shipping activity," Ray added. While Earth intelligence has traditionally been dominated by government and military bodies, a significant shift is underway. Gartner predicts that by 2030, enterprises will account for over 50 per cent of global Earth intelligence spending, up from less than 15 per cent in 2024, overtaking government and military investment. "New economics are evolving because very low Earth orbit (VLEO) satellites are cheaper to build and launch, and these satellites are watching the earth in new ways. Radar and hyperspectral techniques see things which used to be invisible, and revisit times can be an hour or less. Private companies are also experimenting with VLEO, which can deliver a resolution as low as 10 cm. That is small enough to spot a mouse," Gartner said in a press release. "This is where AI plays a critical role. Unlike many domains, there is a plethora of data. But that data needs to be engineered into fit-for-purpose information to feed industry- and function-specific AI models," Ray said. (ANI)
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