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...
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (438 p.) |
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| 035 | |a (CKB)4100000010106105 | ||
| 035 | |a (oapen)https://directory.doabooks.org/handle/20.500.12854/52518 | ||
| 035 | |a (EXLCZ)994100000010106105 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Lee, Saro |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 | ||
| 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. | ||
| 546 | |a English | ||
| 653 | |a artificial neural network | ||
| 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 | ||
| 776 | |z 3-03921-215-X | ||
| 700 | 1 | |a Jung, Hyung-Sup |4 auth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:32:26 Europe/Vienna |f system |c marc21 |a 2020-02-01 22:26:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338817910004498&Force_direct=true |Z 5338817910004498 |b Available |8 5338817910004498 | ||