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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Edited by
September 2019
438 pages
  • ISBN978-3-03921-215-6 (Paperback)
  • ISBN978-3-03921-216-3 (PDF)
https://doi.org/10.3390/books978-3-03921-216-3
Free download (PDF)

This book is a reprint of the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

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.

Format
  • Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
landslide; bagging ensemble; Logistic Model Trees; GIS; Vietnam; colorization; random forest regression; grayscale aerial image; change detection; gully erosion; environmental variables; data mining techniques; SCAI; GIS; mapping; single-class data descriptors; materia medica resource; Panax notoginseng; one-class classifiers; geoherb; change detection; convolutional network; deep learning; panchromatic; remote sensing; remote sensing image segmentation; convolutional neural networks; Gaofen-2; hybrid structure convolutional neural networks; winter wheat spatial distribution; classification-based learning; real-time precise point positioning; convergence time; ionospheric delay constraints; precise weighting; landslide; weights of evidence; logistic regression; random forest; hybrid model; traffic CO; traffic CO prediction; neural networks; GIS; land use/land cover (LULC); unmanned aerial vehicle; texture; gray-level co-occurrence matrix; machine learning; crop; landslide susceptibility; random forest; boosted regression tree; information gain; landslide susceptibility map; ALS point cloud; multi-scale; classification; large scene; coarse particle; particulate matter 10 (PM10); landsat image; machine learning; support vector machine; high-resolution; optical remote sensing; object detection; deep learning; transfer learning; land subsidence; Bayes net; naïve Bayes; logistic; multilayer perceptron; logit boost; change detection; convolutional network; deep learning; panchromatic; remote sensing; leaf area index (LAI); machine learning; Sentinel-2; sensitivity analysis; training sample size; spectral bands; spatial sparse recovery; constrained spatial smoothing; spatial spline regression; alternating direction method of multipliers; landslide prediction; machine learning; neural networks; model switching; spatial predictive models; predictive accuracy; model assessment; variable selection; feature selection; model validation; spatial predictions; reproducible research; Qaidam Basin; remote sensing; TRMM; artificial neural network; n/a