A framework for processing large scale geospatial and remote sensing data in MapReduce environment

• The advancement of the AEGIS geospatial toolkit to enable spatial data processing in MapReduce environment.
• Presentation of high level data and operation environment model, and their application in the Apache Hadoop environment.
• Moving one phase single machine operations into multi-phase processes in a distributed system.
• Feature based comparison of geospatial and image processing tools in the Apache Hadoop environment.

In recent years distributed data processing has reached many areas of computer science including geographic and remote sensing information systems. With the continuing increase of data, existing algorithms and data management need to be moved to new architecture, which may require a great deal of effort.This paper describes a geospatial data processing framework designed to enable the management and processing of spatial and remote sensing data in distributed environment. The framework is based on the mainstream MapReduce paradigm, and its open-source implementation, the Apache Hadoop library. The primary design goals of this framework are extensibility and adaptability, to enable previously implemented algorithms and existing toolkits to be easily adapted to distributed execution without major effort.The possibilities of the framework are demonstrated on remote sensing image processing operations in comparison with existing solutions in the Hadoop environment. Results show that this toolkit provides a much broader possibility to advance geospatial data processing than the currently available solutions.

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