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Table of Contents

Learning From Geospatial Data: Problems and Important Concepts of Machine Learning – Machine Learning Algorithms for Geospatial Data – Contents of the Book. Software Description – Short Review of the Literature / Exploratory Spatial Data Analysis: Presentation of Data and Case Studies: Exploratory Spatial Data Analysis – Data Pre-Processing – Spatial Correlations: Variography – Presentation of Data – k-Nearest Neighbours Algorithm: a Benchmark Model for Regression and Classification / Geostatistics: Spatial Predictions – Geostatistical Conditional Simulations – Spatial Classification – Software / Machine Learning Algorithms: Artificial Neural Networks: Introduction – Radial Basis Function Neural Networks – General Regression Neural Networks – Probabilistic Neural Networks – Self-Organising Maps – Gaussian Mixture Models And Mixture Density Network / Support Vector Machines And Kernel Methods: Introduction to Statistical Learning Theory – Support Vector Classification – Spatial Data Classification with SVM – Support Vector Regression – Spatial Data Mapping with SVR – Advanced Topics in Kernel Methods.

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