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Advanced machine learning and deep learning approaches for remote sensing
Provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology.
Atmospheric Boundary Layers : Nature, Theory, and Application to Environmental Modelling and Security
This collection of peer reviewed papers represents a concise, up-to-date summary of our current knowledge of planetary boundary layer (PBL) physics and parameterization. As such, it makes a major contribution to the interchange of knowledge and ideas between physicists, meteorologists and environmental modellers and sets out the course to be followed in subsequent research to improve PBL parameterizations in climate, numerical weather prediction, air quality, and emergency preparedness models. Major themes covered are: Nature and theory of turbulent boundary layers, Boundary layer flows - modelling and applications to environmental security, Nature, theory and modelling of boundary-layer flows, and Air flows within and above urban and other complex canopies - air-sea-ice interactions.
