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Large-Scale Machine Learning in the Earth Sciences
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Artikel-Nr:
9780367573232
Veröffentl:
2020
Erscheinungsdatum:
30.06.2020
Seiten:
238
Autor:
Ashok N. Srivastava
Gewicht:
508 g
Format:
254x178x23 mm
Sprache:
Deutsch
Beschreibung:
Ashok N. Srivastava, Ph.D. is the VP of Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. He leads a new research and development center in Palo Alto focusing on building products and technologies powered by big data, large-scale machine learning, and analytics. He is an Adjunct Professor at Stanford University in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Dr. Srivastava is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).
Large-scale machine learning could prove highly beneficial in the study of earth science, a broad, multidisciplinary field of study that generates huge amounts of data. This book is the first to tackle the subject, covering significant issues in earth science and large-scale machine learning techniques with well-known authorities in the field.
Computer science. Ecosystems. Climate/hydrology. Climate/landuse. Climate/carbon. Machine learning. Earth science. Estimation and bias correction in aerosols. Land cover monitoring and change detection. Rainfall prediction. Mining spatio-temporal data sets. Climate systems using earth science models. Ocean eddy monitoring. Class imbalance in earth science. Big data and petascale climate science. Machine learning of precipitation forecasts. Forest fire mapping. Morphological neural networks for hyperspectral data using HPC. Machine learning applications for biomass assessments. Grid computing for remote sensing applications. Deep learning for very high resolution imagery classification.