TY - BOOK ID - 42509 TI - Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing AU - Lee, Saro AU - Jung, Hyung-Sup PY - 2019 SN - 9783039212156 9783039212163 DB - DOAB KW - landslide KW - bagging ensemble KW - Logistic Model Trees KW - GIS KW - Vietnam KW - colorization KW - random forest regression KW - grayscale aerial image KW - change detection KW - gully erosion KW - environmental variables KW - data mining techniques KW - SCAI KW - GIS KW - mapping KW - single-class data descriptors KW - materia medica resource KW - Panax notoginseng KW - one-class classifiers KW - geoherb KW - change detection KW - convolutional network KW - deep learning KW - panchromatic KW - remote sensing KW - remote sensing image segmentation KW - convolutional neural networks KW - Gaofen-2 KW - hybrid structure convolutional neural networks KW - winter wheat spatial distribution KW - classification-based learning KW - real-time precise point positioning KW - convergence time KW - ionospheric delay constraints KW - precise weighting KW - landslide KW - weights of evidence KW - logistic regression KW - random forest KW - hybrid model KW - traffic CO KW - traffic CO prediction KW - neural networks KW - GIS KW - land use/land cover (LULC) KW - unmanned aerial vehicle KW - texture KW - gray-level co-occurrence matrix KW - machine learning KW - crop KW - landslide susceptibility KW - random forest KW - boosted regression tree KW - information gain KW - landslide susceptibility map KW - ALS point cloud KW - multi-scale KW - classification KW - large scene KW - coarse particle KW - particulate matter 10 (PM10) KW - landsat image KW - machine learning KW - support vector machine KW - high-resolution KW - optical remote sensing KW - object detection KW - deep learning KW - transfer learning KW - land subsidence KW - Bayes net KW - naïve Bayes KW - logistic KW - multilayer perceptron KW - logit boost KW - change detection KW - convolutional network KW - deep learning KW - panchromatic KW - remote sensing KW - leaf area index (LAI) KW - machine learning KW - Sentinel-2 KW - sensitivity analysis KW - training sample size KW - spectral bands KW - spatial sparse recovery KW - constrained spatial smoothing KW - spatial spline regression KW - alternating direction method of multipliers KW - landslide prediction KW - machine learning KW - neural networks KW - model switching KW - spatial predictive models KW - predictive accuracy KW - model assessment KW - variable selection KW - feature selection KW - model validation KW - spatial predictions KW - reproducible research KW - Qaidam Basin KW - remote sensing KW - TRMM KW - artificial neural network KW - n/a UR - https://www.doabooks.org/doab?func=search&query=rid:42509 AB - 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. ER -