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Rasterhadoop: An Application Perspective of Raster Data Processing on Hadoop

Hadoop is currently the most popular platform for parallel processing. With its two major components namely the Distributed File System (HDFS) and a parallel processing paradigm (MapReduce) in addition to its ease of installation and usage, Hadoop has become the chosen platform for efficiency whether in the commercial arena or the scientific arena such as Satellite Data Processing. The number of remote sensing satellites have also grown leaps and bounds and the data sent back by them for processing has all the three characteristics namely volume, velocity and variety that make it Big Spatial Data. In this paper, we present the extensions provided to Hadoop that enable Image Processing using legacy code and further elaborate on the various methods provided.

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

R. Phani Bhushan* DVLN Somayajulu S. Venkatraman RBV Subramanayam

International Journal of Recent Technology and Engineering (IJRTE)

2019

Title: Rasterhadoop: An Application Perspective of Raster Data Processing on Hadoop

Description:

Hadoop is currently the most popular platform for parallel processing.

With its two major components namely the Distributed File System (HDFS) and a parallel processing paradigm (MapReduce) in addition to its ease of installation and usage, Hadoop has become the chosen platform for efficiency whether in the commercial arena or the scientific arena such as Satellite Data Processing.

The number of remote sensing satellites have also grown leaps and bounds and the data sent back by them for processing has all the three characteristics namely volume, velocity and variety that make it Big Spatial Data.

In this paper, we present the extensions provided to Hadoop that enable Image Processing using legacy code and further elaborate on the various methods provided.

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Rasterhadoop: An Application Perspective of Raster Data Processing on Hadoop

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