Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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 netw...

Full description

Saved in:
Bibliographic Details
Main Author: Lee, Saro (auth)
Other Authors: Jung, Hyung-Sup (auth)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:
n/a
GIS
Online Access:DOAB: download the publication
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000naaaa2200000uu 4500
001 doab_20_500_12854_52518
005 20210211
003 oapen
006 m o d
007 cr|mn|---annan
008 20210211s2019 xx |||||o ||| 0|eng d
020 |a books978-3-03921-216-3 
020 |a 9783039212156 
020 |a 9783039212163 
040 |a oapen  |c oapen 
024 7 |a 10.3390/books978-3-03921-216-3  |c doi 
041 0 |a eng 
042 |a dc 
072 7 |a TDCW  |2 bicssc 
100 1 |a Lee, Saro  |4 auth 
700 1 |a Jung, Hyung-Sup  |4 auth 
245 1 0 |a Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing 
260 |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2019 
300 |a 1 electronic resource (438 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by-nc-nd/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ 
546 |a English 
650 7 |a Pharmaceutical technology  |2 bicssc 
653 |a artificial neural network 
653 |a n/a 
653 |a model switching 
653 |a sensitivity analysis 
653 |a neural networks 
653 |a logit boost 
653 |a Qaidam Basin 
653 |a land subsidence 
653 |a land use/land cover (LULC) 
653 |a naïve Bayes 
653 |a multilayer perceptron 
653 |a convolutional neural networks 
653 |a single-class data descriptors 
653 |a logistic regression 
653 |a feature selection 
653 |a mapping 
653 |a particulate matter 10 (PM10) 
653 |a Bayes net 
653 |a gray-level co-occurrence matrix 
653 |a multi-scale 
653 |a Logistic Model Trees 
653 |a classification 
653 |a Panax notoginseng 
653 |a large scene 
653 |a coarse particle 
653 |a grayscale aerial image 
653 |a Gaofen-2 
653 |a environmental variables 
653 |a variable selection 
653 |a spatial predictive models 
653 |a weights of evidence 
653 |a landslide prediction 
653 |a random forest 
653 |a boosted regression tree 
653 |a convolutional network 
653 |a Vietnam 
653 |a model validation 
653 |a colorization 
653 |a data mining techniques 
653 |a spatial predictions 
653 |a SCAI 
653 |a unmanned aerial vehicle 
653 |a high-resolution 
653 |a texture 
653 |a spatial sparse recovery 
653 |a landslide susceptibility map 
653 |a machine learning 
653 |a reproducible research 
653 |a constrained spatial smoothing 
653 |a support vector machine 
653 |a random forest regression 
653 |a model assessment 
653 |a information gain 
653 |a ALS point cloud 
653 |a bagging ensemble 
653 |a one-class classifiers 
653 |a leaf area index (LAI) 
653 |a landslide susceptibility 
653 |a landsat image 
653 |a ionospheric delay constraints 
653 |a spatial spline regression 
653 |a remote sensing image segmentation 
653 |a panchromatic 
653 |a Sentinel-2 
653 |a remote sensing 
653 |a optical remote sensing 
653 |a materia medica resource 
653 |a GIS 
653 |a precise weighting 
653 |a change detection 
653 |a TRMM 
653 |a traffic CO 
653 |a crop 
653 |a training sample size 
653 |a convergence time 
653 |a object detection 
653 |a gully erosion 
653 |a deep learning 
653 |a classification-based learning 
653 |a transfer learning 
653 |a landslide 
653 |a traffic CO prediction 
653 |a hybrid model 
653 |a winter wheat spatial distribution 
653 |a logistic 
653 |a alternating direction method of multipliers 
653 |a hybrid structure convolutional neural networks 
653 |a geoherb 
653 |a predictive accuracy 
653 |a real-time precise point positioning 
653 |a spectral bands 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/1533  |7 0  |z DOAB: download the publication 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/52518  |7 0  |z DOAB: description of the publication