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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

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.

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

Earth Observation Data Cubes

Authors: --- --- ---
ISBN: 9783039280926 9783039280933 Year: Pages: 302 DOI: 10.3390/books978-3-03928-093-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2020-04-07 23:07:09
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Abstract

Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10

Keywords

topology based map algebra --- data cubes --- big data --- map algebra --- earth oberservation --- GRASS GIS --- earth observations --- satellite imagery --- R --- data cubes --- Sentinel-2 --- Sentinel-1 --- SAR --- analysis ready data --- ARD --- interoperability --- data cube --- Earth observation --- pyroSAR --- data cube --- image cube --- image data cube --- imagery --- Landsat --- Sentinel --- earth observation --- GIS --- web services --- web application --- analysis --- GIS --- Open Data Cube --- Earth Observations --- interoperability --- visualization --- Sentinel --- Analysis Ready Data --- Sentinel-1 --- Synthetic Aperture Radar --- Data Cube --- dual-polarimetric decomposition --- interferometric coherence --- Digital Earth Australia --- remote sensing --- big Earth data --- big EO data --- information extraction --- semantic enrichment --- time-series --- Open Data Cube --- remote sensing --- geospatial standards --- landsat --- sentinel --- analysis ready data --- dynamic data citation --- subset --- data curation --- persistent identifier --- data provenance --- metadata --- versioning --- query store --- data sharing --- FAIR principles --- big earth data --- sustainable development goals --- swiss DC --- Armenian DC --- Landsat --- sentinel --- analysis ready data --- data discovery --- metadata --- knowledge base --- graph data --- intelligent semantic agents --- data cube --- optical remote sensing --- snow cover --- Gran Paradiso National Park --- climate change --- land cover classification --- change --- Digital Earth Australia --- open data cube --- Landsat --- Australia --- Open Data Cube --- UN 2030 Agenda for Sustainable Development --- UN System of Environmental Economic Accounting --- Earth observation data --- open science --- reproducibility --- earth observations --- data cube --- analysis ready data --- remote sensing --- satellite imagery

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