Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI

Abstract—Vectorization processes focus on grouping pixels of
a raster image into raw line segments, and forming lines,
polylines or polygons. To vectorize massive raster images
regarding resource and performance problems, we use a
distributed HIPI image processing interface based on
MapReduce approach. Apache Hadoop is placed at the core of
the framework. To realize such a system, we first define mapper
function, and then its input and output formats. In this paper,
mappers convert raster mosaics into vector counterparts.
Reducer functions are not needed for vectorization. Vector
representations of the raster images is expected to give better
performance in distributed computations by reducing the
negative effects of bandwidth problem and horizontal scalability
analysis is done.



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