LEADER 05016nam 2201381z- 450 001 9910367564103321 005 20231214132827.0 010 $a3-03921-216-8 035 $a(CKB)4100000010106105 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/52518 035 $a(EXLCZ)994100000010106105 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Techniques Applied to Geoscience Information System and Remote Sensing 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (438 p.) 311 $a3-03921-215-X 330 $aAs 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. 610 $aartificial neural network 610 $amodel switching 610 $asensitivity analysis 610 $aneural networks 610 $alogit boost 610 $aQaidam Basin 610 $aland subsidence 610 $aland use/land cover (LULC) 610 $anai?ve Bayes 610 $amultilayer perceptron 610 $aconvolutional neural networks 610 $asingle-class data descriptors 610 $alogistic regression 610 $afeature selection 610 $amapping 610 $aparticulate matter 10 (PM10) 610 $aBayes net 610 $agray-level co-occurrence matrix 610 $amulti-scale 610 $aLogistic Model Trees 610 $aclassification 610 $aPanax notoginseng 610 $alarge scene 610 $acoarse particle 610 $agrayscale aerial image 610 $aGaofen-2 610 $aenvironmental variables 610 $avariable selection 610 $aspatial predictive models 610 $aweights of evidence 610 $alandslide prediction 610 $arandom forest 610 $aboosted regression tree 610 $aconvolutional network 610 $aVietnam 610 $amodel validation 610 $acolorization 610 $adata mining techniques 610 $aspatial predictions 610 $aSCAI 610 $aunmanned aerial vehicle 610 $ahigh-resolution 610 $atexture 610 $aspatial sparse recovery 610 $alandslide susceptibility map 610 $amachine learning 610 $areproducible research 610 $aconstrained spatial smoothing 610 $asupport vector machine 610 $arandom forest regression 610 $amodel assessment 610 $ainformation gain 610 $aALS point cloud 610 $abagging ensemble 610 $aone-class classifiers 610 $aleaf area index (LAI) 610 $alandslide susceptibility 610 $alandsat image 610 $aionospheric delay constraints 610 $aspatial spline regression 610 $aremote sensing image segmentation 610 $apanchromatic 610 $aSentinel-2 610 $aremote sensing 610 $aoptical remote sensing 610 $amateria medica resource 610 $aGIS 610 $aprecise weighting 610 $achange detection 610 $aTRMM 610 $atraffic CO 610 $acrop 610 $atraining sample size 610 $aconvergence time 610 $aobject detection 610 $agully erosion 610 $adeep learning 610 $aclassification-based learning 610 $atransfer learning 610 $alandslide 610 $atraffic CO prediction 610 $ahybrid model 610 $awinter wheat spatial distribution 610 $alogistic 610 $aalternating direction method of multipliers 610 $ahybrid structure convolutional neural networks 610 $ageoherb 610 $apredictive accuracy 610 $areal-time precise point positioning 610 $aspectral bands 700 $aLee$b Saro$4auth$01281248 702 $aJung$b Hyung-Sup$4auth 906 $aBOOK 912 $a9910367564103321 996 $aMachine Learning Techniques Applied to Geoscience Information System and Remote Sensing$93018421 997 $aUNINA