Artificial Intelligence-Based Learning Approaches for Remote Sensing

Dear Colleagues,

Remote sensing is a tool for comprehending the earth and supporting human–earth communications. Recent advances in remote sensing have led to a high-resolution monitoring of Earth on a global scale, providing a massive amount of earth observation data. These data must be processed with new levels of accuracy, complexity, security, achievement, and reliability. Therefore, applicable and consistent research on artificial intelligence-based learning methods and its applied image processing are needed for remote sensing. These methods can be general and specific tools of artificial intelligence, including regression models, neural networks, decision trees, information retrieval, reinforcement learning, graphical models, and decision processes. We trust that artificial intelligence, deep learning and data science methods will provide promising tools to overcome many challenging issues in remote sensing in terms of accuracy and reliability. This Special Issue is the second edition of “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”. In this second edition, our new Special Issue aims to report the latest advances and trends concerning the advanced artificial learning and data science techniques to the remote sensing data processing issues. Papers of both theoretical and applicative nature, as well as contributions regarding new advanced artificial learning and data science techniques for the remote sensing research community, are welcome.

Dr. Gwanggil Jeon
Guest Editor

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