This chapter explores the integration of artificial intelligence techniques within earth observation (EO) data analysis, leveraging scalable computing infrastructure to enhance capabilities in processing and deriving insights from vast EO datasets. The overview encompasses the significance of AI in EO by highlighting its applications. It also examines diverse data sources utilized in EO, ranging from data repositories to satellite imagery and in-situ data. Furthermore, the chapter elaborates on AI algorithms tailored for EO analysis, covering machine learning algorithms such as supervised, unsupervised, reinforcement, and deep learning techniques like neural networks. Additionally, the chapter discusses various computing techniques, including high-performance, distributed and cloud computing.

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AI for Earth Observation

  • Hrachya Astsatryan,
  • Arthur Lalayan,
  • Gregory Giuliani

摘要

This chapter explores the integration of artificial intelligence techniques within earth observation (EO) data analysis, leveraging scalable computing infrastructure to enhance capabilities in processing and deriving insights from vast EO datasets. The overview encompasses the significance of AI in EO by highlighting its applications. It also examines diverse data sources utilized in EO, ranging from data repositories to satellite imagery and in-situ data. Furthermore, the chapter elaborates on AI algorithms tailored for EO analysis, covering machine learning algorithms such as supervised, unsupervised, reinforcement, and deep learning techniques like neural networks. Additionally, the chapter discusses various computing techniques, including high-performance, distributed and cloud computing.