Reprint

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Edited by
September 2019
438 pages
  • ISBN978-3-03921-215-6 (Paperback)
  • ISBN978-3-03921-216-3 (PDF)

This is a Reprint of the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

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