Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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.
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 Special Issue of Applied Sciences on the machine learning techniques applied to geoscience information systems and remote sensing aims to attract novel contributions. We have invited original research papers addressing the state-of-the-art in the following areas:
1) Application of machine learning techniques combined with GIS;
2) Application of machine learning techniques to remote sensing;
3) Application of machine learning techniques to global positioning system (GPS);
4) Spatial analysis and geocomputation based on machine learning techniques;
5) Spatial prediction using machine learning techniques;
6) Data processing of geoinformation using machine learning techniques;
7) Comparative analysis among several machine learning techniques applied to GIS and RS;
8) Application of machine learning techniques on geosciences, environments, natural hazards, and
natural resources as case studies.
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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing Fast Download
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