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Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics.

Geoinformatics. --- Neural networks (Computer science)

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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.

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

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This edited volume is based on the best papers accepted for presentation during the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018. The book compiles a wide range of topics addressing various issues by experienced researchers mainly from research institutes in the Mediterranean, MENA region, North America and Asia. Remote sensing observations can close gaps in information scarcity by complementing ground-based sparse data. Spatial, spectral, temporal and radiometric characteristics of satellites sensors are most suitable for features identification. The local to global nature and broad spatial scale of remote sensing with the wide range of spectral coverage are essential characteristics, which make satellites an ideal platform for mapping, observation, monitoring, assessing and providing necessary mitigation measures and control for different related Earth's systems processes. Main topics in this book include: Geo-informatics Applications, Land Use / Land Cover Mapping and Change Detection, Emerging Remote Sensing Applications, Rock Formations / Soil Lithology Mapping, Vegetation Mapping Impact and Assessment, Natural Hazards Mapping and Assessment, Ground Water Mapping and Assessment, Coastal Management of Marine Environment and Atmospheric Sensing.

Nature protection --- Geophysics --- Meteorology. Climatology --- Geology. Earth sciences --- Hydrobiology --- Water supply. Water treatment. Water pollution --- Environmental protection. Environmental technology --- Hunting. Fishery. Aquaculture --- atmosfeerchemie --- atmosfeerfysica --- aquacultuur --- metrologie --- hydrobiologie --- remote sensing --- water --- waterverontreiniging --- wetenschappen --- milieuverontreiniging --- geologie --- natuurrampen --- atmosfeer