Application of Artificial Neural Networks in Geoinformatics

© 2019 by the authors; CC BY license

severity prediction; GIS; traffic accidents; deep learning; recurrent neural networks; spatial data mining; SVM; ANN; validation; ROC; landslide detection; LiDAR; recurrent neural networks (RNN); multi‐layer perceptron neural networks (MLP‐NN); GIS; remote sensing; earthquake; damage assessment; neural networks; satellite data; SAR; Sentinel-2; road safety; risk evaluation; data envelopment analysis; artificial neural networks; crash data analysis; cross-validation; multi-layer perceptron; remote sensing; classification error; sample design; machine learning; habitat mapping; marten; leopard cat; ANN; South Korea; artificial neural network; traffic monitoring; GPS; GIS; mode detection; Sinabung eruption; Merapi eruption; pyroclastic flow deposits; Landsat imagery; artificial neural network; air pollution; artificial neural network; genetic algorithms; surface ozone; threshold models; synthetic aperture radar (SAR); ship detection; artificial neural network (ANN); Kompsat-5; oil spill; polarimetric synthetic aperture radar (SAR); deep belief network; autoencoder; remote sensing; landslide susceptibility; artificial neural network; boosted tree; landslide inventory; forestry vertical structure; stratification; forest inventory; aerial orthophoto; lidar (light detection and ranging); ANN (Artificial Neural Network); machine learning; n/a