AI/ML and Enabling Technologies in Earth Observation

"Earth observation service providers must embrace intelligent, decentralised and adaptive systems that enable real-time data analytics if they wish to stay competitive in the evolving Earth observation market."

This report provides strategic guidance about using AI and machine learning (ML) for Earth observation (EO). It also describes how AI/ML can be used together with emerging enabling technologies such as foundation models to offer tailored solutions for end users. It outlines implementation strategies for various stakeholder groups, and lists the benefits of, and requirements for, fulfilling customers' needs.

Vendors can also use the recommendations to further strengthen their value propositions (particularly for downstream applications) and build solutions that address market needs.

Questions answered in this report:

• How can EO satellite operators and service providers use agentic AI, federated learning and foundation models to offer high-performance, tailored solutions?
• What should various stakeholders do to address, and benefit from, the increasing adoption of AI?
• What are the adoption strategies for edge computing in space and on the ground?
• Which partnerships will enable stakeholders to enhance and improve their AI/ML capabilities?
• What are the key considerations when building AI-enabled EO solutions?