05471nam 22015133a 450 991036756410332120250203235429.09783039212163303921216810.3390/books978-3-03921-216-3(CKB)4100000010106105(oapen)https://directory.doabooks.org/handle/20.500.12854/52518(ScCtBLL)91259e84-9255-4174-ab10-33359fdc7c21(OCoLC)1163809718(oapen)doab52518(EXLCZ)99410000001010610520250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning Techniques Applied to Geoscience Information System and Remote SensingHyung-Sup Jung, Saro LeeMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (438 p.)9783039212156 303921215X 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.Pharmaceutical chemistry and technologybicsscartificial neural networkmodel switchingsensitivity analysisneural networkslogit boostQaidam Basinland subsidenceland use/land cover (LULC)naïve Bayesmultilayer perceptronconvolutional neural networkssingle-class data descriptorslogistic regressionfeature selectionmappingparticulate matter 10 (PM10)Bayes netgray-level co-occurrence matrixmulti-scaleLogistic Model TreesclassificationPanax notoginsenglarge scenecoarse particlegrayscale aerial imageGaofen-2environmental variablesvariable selectionspatial predictive modelsweights of evidencelandslide predictionrandom forestboosted regression treeconvolutional networkVietnammodel validationcolorizationdata mining techniquesspatial predictionsSCAIunmanned aerial vehiclehigh-resolutiontexturespatial sparse recoverylandslide susceptibility mapmachine learningreproducible researchconstrained spatial smoothingsupport vector machinerandom forest regressionmodel assessmentinformation gainALS point cloudbagging ensembleone-class classifiersleaf area index (LAI)landslide susceptibilitylandsat imageionospheric delay constraintsspatial spline regressionremote sensing image segmentationpanchromaticSentinel-2remote sensingoptical remote sensingmateria medica resourceGISprecise weightingchange detectionTRMMtraffic COcroptraining sample sizeconvergence timeobject detectiongully erosiondeep learningclassification-based learningtransfer learninglandslidetraffic CO predictionhybrid modelwinter wheat spatial distributionlogisticalternating direction method of multipliershybrid structure convolutional neural networksgeoherbpredictive accuracyreal-time precise point positioningspectral bandsPharmaceutical chemistry and technologyJung Hyung-Sup1786960Lee SaroScCtBLLScCtBLLBOOK9910367564103321Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing4319519UNINA