Geoscience Information

The International Conference on Geological Information represents the first major attempt to bring together geoscience information specialists from allover the world. The purpose of the conference was to assess the current state-of the-art in geoscience information from both the regional and functional point of view. It was hoped that the conference could take steps to bring about increased international cooperation and collaboration in the field of geological information. The papers ranged over the whole spec trum of documentation from primary publishing back to the user, including data. Perhaps a keyword for the conference might be "cooperation". The idea of, and need for, cooperation was stressed in almost every talk. The final panel session was devoted to a discussion on the formation of a proposed International Association for Geological Information. Despite the growing pressure on information managers, stimulated by increasing international activities in geology, the global perspective of plate tectonics and worldwide concern for the availability of non-renewable resources, there does not exist an international organisation specifi cally concerned with geological information. Delegates agreed that there was no need for a new professional society of individuals but that a federation or similar organisation might be desirable. In the final session it became apparent that if the geological information community is to make the best use of all the systems and developments available there is very clearly a need to know what exists in all these areas at present. An urgent task is to identify these systems.

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.

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.