Machine Learning Techniques Applied to Geoscience Information System and Remote SensingHyung-Sup Jung, Saro Lee 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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accuracy activity addition algorithm analysis applications approach assessment average better calculated change detection China classification color combination compared comparison Comput constructed convolutional crop CrossRef dataset deep determine developed distance distribution effect Environ error estimation evaluation extraction factors feature field Figure function improve increased indicates input Italy land landslide susceptibility mapping layer locations logistic machine learning mean measures method neural network object observed obtained optimal output parameters performance pixels positive precipitation prediction predictive modeling problem proposed random forest reference region regression remote sensing represents respectively samples scene Science selection Sens shown shows slope soil South Korea spatial spatial predictive spectral stations statistical study area Table techniques texture traffic tree urban validation values variables vector weight