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人工智慧/机器学习（AI/ML）和支援技术：地球观测服务提供者的策略
市场调查报告书
商品编码
1527365

人工智慧/机器学习（AI/ML）和支援技术：地球观测服务提供者的策略

AI/ML and Enabling Technologies: Strategies for Earth Observation Service Providers
出版日期: | 出版商: Analysys Mason | 英文 14 Slides | 商品交期: 最快1-2个工作天内

价格
简介目录

"多模式卫星资料的复杂性及其提供的见解推动EO服务提供者投资人工智慧和机器学习解决方案的需求。"

目前人工智慧（AI）在地球观测（EO）的应用仅限于简单的自动化，并没有充分利用人工智慧的自学习能力。此外，人工智慧在下游分析应用中并未充分利用。

本报告建议EO服务供应商采用人工智慧的策略，以及如何将其与基础模型等新技术结合使用，为最终用户提供量身定制的解决方案，将考察如何将人工智慧与基础模型等新技术结合起来。

本报告回答的问题：

  • 目前人工智慧（AI）在地球观测（EO）中的采用程度如何？
  • 基础模型发挥什么作用？
  • 生成式人工智慧（GenAI）如何协助建立客製化解决方案并为下游分析公司实现价值差异化？
  • 每个利害关係人应该如何应对人工智慧的日益普及并从这一新的市场趋势中受益？
  • 什么样的合作关係可以帮助这些利害关係人加强和提高他们的 AI/ML 能力？
简介目录

"The complexity of multi-modal satellite data, and the insights we can derive from it, make it increasingly necessary for EO service providers to invest in AI and ML solutions."

The current adoption of AI in Earth observation (EO) is limited to simple automation and does not fully take advantage of the self-learning capabilities of artificial intelligence (AI). Furthermore, AI is underused in downstream analytics applications.

This report provides strategic guidance for EO service providers on adopting AI and how AI can be used together with emerging technologies such as foundation models to offer tailored solutions for end users.

Questions answered in this report:

  • What is the current level of adoption of AI in EO?
  • What role do foundation models play?
  • How can generative AI (GenAI) help to build tailored solutions and enable value differentiation for downstream analytics players?
  • What should different stakeholder groups do to address the increasing adoption of AI and benefit from this emerging market trend?
  • Which partnerships will enable these stakeholders to enhance and improve their AI/ML capabilities?