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210512 ||| eng |
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|a 9783039212156
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|a books978-3-03921-216-3
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|a 9783039212163
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1 |
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|a Lee, Saro
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245 |
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|a Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
|h Elektronische Ressource
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260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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300 |
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|a 1 electronic resource (438 p.)
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653 |
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|a machine learning
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|a landsat image
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653 |
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|a logistic regression
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653 |
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|a unmanned aerial vehicle
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653 |
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|a classification-based learning
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653 |
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|a feature selection
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653 |
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|a landslide prediction
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653 |
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|a training sample size
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653 |
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|a deep learning
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653 |
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|a landslide susceptibility
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653 |
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|a spatial predictive models
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653 |
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|a hybrid structure convolutional neural networks
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653 |
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|a neural networks
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653 |
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|a bagging ensemble
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653 |
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|a precise weighting
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653 |
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|a Pharmaceutical chemistry and technology / bicssc
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653 |
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|a traffic CO prediction
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653 |
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|a GIS
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|a colorization
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653 |
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|a data mining techniques
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|a Qaidam Basin
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653 |
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|a artificial neural network
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|a multi-scale
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|a Bayes net
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|a materia medica resource
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|a Gaofen-2
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|a SCAI
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|a constrained spatial smoothing
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653 |
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|a particulate matter 10 (PM10)
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|a model assessment
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653 |
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|a large scene
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653 |
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|a spatial spline regression
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653 |
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|a convolutional neural networks
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653 |
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|a landslide
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|a alternating direction method of multipliers
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|a support vector machine
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|a reproducible research
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|a spectral bands
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|a Panax notoginseng
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|a multilayer perceptron
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|a environmental variables
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|a texture
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|a remote sensing image segmentation
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|a gray-level co-occurrence matrix
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653 |
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|a variable selection
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653 |
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|a change detection
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|a hybrid model
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|a convolutional network
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|a logistic
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|a gully erosion
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|a Sentinel-2
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|a geoherb
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|a boosted regression tree
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|a high-resolution
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|a random forest
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|a mapping
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|a n/a
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|a spatial sparse recovery
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|a model validation
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|a spatial predictions
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|a logit boost
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|a model switching
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|a optical remote sensing
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|a predictive accuracy
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|a grayscale aerial image
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|a traffic CO
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|a ionospheric delay constraints
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653 |
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|a Vietnam
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|a one-class classifiers
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|a land use/land cover (LULC)
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653 |
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|a panchromatic
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|a object detection
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653 |
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|a transfer learning
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|a TRMM
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|a crop
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|a weights of evidence
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|a naïve Bayes
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|a random forest regression
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|a sensitivity analysis
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|a single-class data descriptors
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|a convergence time
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|a land subsidence
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|a remote sensing
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|a winter wheat spatial distribution
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|a classification
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|a leaf area index (LAI)
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|a coarse particle
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|a landslide susceptibility map
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|a information gain
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|a Logistic Model Trees
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|a ALS point cloud
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|a real-time precise point positioning
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700 |
1 |
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|a Jung, Hyung-Sup
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041 |
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7 |
|a eng
|2 ISO 639-2
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989 |
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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028 |
5 |
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|a 10.3390/books978-3-03921-216-3
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856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/52518
|z DOAB: description of the publication
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856 |
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|u https://www.mdpi.com/books/pdfview/book/1533
|7 0
|x Verlag
|3 Volltext
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|a 363
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|a 000
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|a 540
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|a 658
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|a 700
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|a 600
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|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.
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