Issues and Challenges of Raster Data Processing in Big Data Environment

Abstract

The raster information model is a broadly used strategy for putting away geographic information. The model most usually appears as a grid-like structure that holds values at regularly separated spans over the extent of the raster. Raster are particularly appropriate for putting away constant information, for example, temperature and height values, however can hold discrete and all out information, for example, land use also. The goal of a raster is given in linear units or angular units and characterizes the extent along one side of the grid cell. High res- olution raster have nearly nearer separating and more network cells than low resolution raster, and require moderate more memory to store.The objective is to develop raster benchmark that is for evaluating spatial analysis on big data platforms that address significant gap for which first to develop a complementary benchmark we will take dataset. Likewise work looking at the stages will be reached out into various computing conditions, Ultimately the assessment benchmark will be refreshed with additional stages, new variants of existing stages, bigger multispectral datasets and coordination of spatial work processes. By this it would be beneficial for high performance geospatial Computing [8]. Dynamic exploration in the space is situated toward further developing pressure plans and execution for elective cell shapes, and better supporting multi-resolution raster capacity and examination capabilities.