Description
Big Data
Techniques and Technologies in Geoinformatics
Coordinator: Karimi Hassan A.
Language: EnglishSubjects for Big Data:
Keywords
Geospatial Big Data; Big Data; Point Pattern Analysis; Line Pattern Analysis; Area Pattern Analysis; Big Data Challenge; Data Science; GES Disc; Geostatistics; Spatial Data Mining; OGC Standard; Computational Geometry; Computational Topology; Big Spatial Data; Machine Learning; Deep Learning; Gps Trace; Remote Sensing; Big Earth Data; Geoprocessing Modeling; LiDAR Data; Geospatial Web Service; VGI Data Set; TRMM Product; VGI Application; Data Sets; TRMM Data; Gps Trajectory; GEOSS Clearinghouse; Time Complexity; Crowdsourced Data; Residence Mode; Frequent Sequences; Sensor Web; TRMM Satellite; Online Algorithms
Publication date: 04-2017
· 15.6x23.4 cm · Paperback
Publication date: 02-2014
· 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Readership
/li>Biography
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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.
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.