Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Beschreibung

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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Saro Lee

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landslide bagging ensemble Logistic Model Trees GIS Vietnam colorization random forest regression grayscale aerial image change detection gully erosion environmental variables data mining techniques SCAI GIS mapping

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