Lee, Saro <br/><br/>
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing 

 -  MDPI - Multidisciplinary Digital Publishing Institute  2019 
 - 1 electronic resource (438 p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>books978-3-03921-216-3 9783039212156 9783039212163 
<br/><br/><label>Standard No.: </label>10.3390/books978-3-03921-216-3  doi 
<br/><br/><label>Subjects--Index Terms: </label>artificial neural network n/a model switching sensitivity analysis neural networks logit boost Qaidam Basin land subsidence land use/land cover (LULC) naïve Bayes multilayer perceptron convolutional neural networks single-class data descriptors logistic regression feature selection mapping particulate matter 10 (PM10) Bayes net gray-level co-occurrence matrix multi-scale Logistic Model Trees classification Panax notoginseng large scene coarse particle grayscale aerial image Gaofen-2 environmental variables variable selection spatial predictive models weights of evidence landslide prediction random forest boosted regression tree convolutional network Vietnam model validation colorization data mining techniques spatial predictions SCAI unmanned aerial vehicle high-resolution texture spatial sparse recovery landslide susceptibility map machine learning reproducible research constrained spatial smoothing support vector machine random forest regression model assessment information gain ALS point cloud bagging ensemble one-class classifiers leaf area index (LAI) landslide susceptibility landsat image ionospheric delay constraints spatial spline regression remote sensing image segmentation panchromatic Sentinel-2 remote sensing optical remote sensing materia medica resource GIS precise weighting change detection TRMM traffic CO crop training sample size convergence time object detection gully erosion deep learning classification-based learning transfer learning landslide traffic CO prediction hybrid model winter wheat spatial distribution logistic alternating direction method of multipliers hybrid structure convolutional neural networks geoherb predictive accuracy real-time precise point positioning spectral bands