地球观测中的人工智慧/机器学习及赋能技术

"地球观测服务供应商如果想在不断发展的地球观测市场中保持竞争力，就必须采用能够进行即时资料分析的智慧、分散式自适应系统。"

本报告为在地球观测 (EO) 中使用人工智慧和机器学习 (ML) 提供了策略指导。它还解释瞭如何将人工智慧/机器学习与基础模型等新兴赋能技术相结合，为最终用户提供客製化的解决方案。

报告中解答的问题

• 地球观测卫星营运商和服务提供者如何利用代理人工智慧、联邦学习和基础模型来提供高效能的客製化解决方案？
• 各利害关係人该如何因应人工智慧日益普及并从中受益？
• 在太空和地面采用边缘运算的策略有哪些？
• 利害关係人可以利用哪些合作关係来增强和提升其人工智慧/机器学习能力？
• 在建构支援 AI 的 EO 解决方案时应考虑哪些因素？

"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?