Big Data : Techniques and Technologies in Geoinformatics book cover
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Big Data
Techniques and Technologies in Geoinformatics

Edited By

Hassan A. Karimi





ISBN 9781138073197
Published March 29, 2017 by CRC Press
312 Pages 111 B/W Illustrations

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Book Description

Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data.

Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information.

With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.

Table of Contents

Part I: Geospatial Data Collection and Applications. Advanced Geospatial Data Collection Technologies. Geo-Crowdsourcing: A New Trend in Collecting Geospatial Data. Big Data in Location-Based Services. Big Data in Satellite Imagery. Part II: Geospatial Data Analytics. Geostatistics. Geospatial Data Mining. Machine Learning. Geovisualization. Part III: Data-Intensive Geospatial Computing. Distributed Geospatial Data-Intensive Computing. Grid Computing for Geospatial Data-Intensive Problems. Cloud Computing for Geospatial Data-Intensive Problems. Parallel Computing for Geospatial Data-Intensive Problems.

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