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

TL;DR

This paper presents a distributed system using HIPI and Hadoop to efficiently vectorize large raster satellite images, improving performance and scalability in processing massive datasets.

Contribution

It introduces a novel MapReduce-based framework for raster image vectorization that eliminates the need for reduce functions, enhancing distributed processing efficiency.

Findings

Effective vectorization of large raster images using HIPI and Hadoop.

Improved performance and scalability in distributed image processing.

Reduction of bandwidth issues in large-scale satellite image analysis.

Abstract

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.