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

by   Suleyman Eken, et al.

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse 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. Reduc functions are not needed for vectorization. Vector representations of 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.



page 1

page 3


An Approach For Stitching Satellite Images In A Bigdata Mapreduce Framework

In this study we present a two-step map/reduce framework to stitch satel...

A MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images

We propose a framework stitching of vector representations of large scal...

Photographic dataset: random peppercorns

This is a photographic dataset collected for testing image processing al...

The fortresses of Ejin: an example of outlining a site from satellite images

From 1960's to 1970's, the Chinese Army built some fortified artificial ...

Image Synthesis and Style Transfer

Affine transformation, layer blending, and artistic filters are popular ...

A Hierarchical Distributed Processing Framework for Big Image Data

This paper introduces an effective processing framework nominated ICP (I...

Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets

It is now common to process volumetric biomedical images using 3D Convol...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.