Emerging Ai Applications In Earth Sciences
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Emerging AI Applications in Earth Sciences: Challenges, Impact and Analysis
This proposed book provides deeper insights into artificial intelligence techniques and procedures available for earth sciences. This book unveils several applications of metaheuristic approaches (i.e., swarm intelligence and IoT technologies) in collaboration with AI for earth sciences. It presents the science behind smart technologies that reveal the power of artificial intelligence and IoT. These methodologies help to extract meaningful insights from earth sciences big data analytics. These advanced technologies used in earth science practices can remove geographical barriers, locally adaptive, operationally feasible, and economically affordable. The areas can be explored with the aim of digitizing the whole world. Technological advancement also impacts the financial aspect involved in managing the earth sciences. Intelligent AI applications have made significant strides in the field of earth sciences, offering novel solutions to complex challenges, driving impactful research, and revolutionizing data analysis and interpretation. This intersection of artificial intelligence and earth sciences has paved the way for an enhanced understanding of our planet and its various phenomena.
Emerging AI Applications in Earth Sciences
This proposed book provides deeper insights into artificial intelligence techniques and procedures available for earth sciences. This book unveils several applications of metaheuristic approaches (i.e., swarm intelligence and IoT technologies) in collaboration with AI for earth sciences. It presents the science behind smart technologies that reveal the power of artificial intelligence and IoT. These methodologies help to extract meaningful insights from earth sciences big data analytics. These advanced technologies used in earth science practices can remove geographical barriers, locally adaptive, operationally feasible, and economically affordable. The areas can be explored with the aim of digitizing the whole world. Technological advancement also impacts the financial aspect involved in managing the earth sciences. Intelligent AI applications have made significant strides in the field of earth sciences, offering novel solutions to complex challenges, driving impactful research, and revolutionizing data analysis and interpretation. This intersection of artificial intelligence and earth sciences has paved the way for an enhanced understanding of our planet and its various phenomena.
Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls
language: en
Publisher: John Wiley & Sons
Release Date: 2021-08-18
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.