A Program for Handling Map Projections of Small Scale Geospatial Raster Data

Abstract

Scientists routinely accomplish small-scale geospatial modeling using raster datasets of global extent. Such use often requires the projection of global raster datasets onto a map or the reprojection from a given map projection associated with a dataset. The distortion characteristics of these projection transformations can have significant effects on modeling results. Distortions associated with the reprojection of global data are generally greater than distortions associated with reprojections of larger-scale, localized areas. The accuracy of areas in projected raster datasets of global extent is dependent on resolution. To address these problems of projection and the associated resampling that accompanies it, methods for framing the transformation space, direct point-to-point transformations rather than gridded transformation spaces, a solution to the wrap-around problem, and an approach to alternative resampling methods are presented. The implementations of these methods are provided in an open source software package called MapImage (or mapIMG, for short), which is designed to function on a variety of computer architectures.

A Lightweight CUDA-Based Parallel Map Reprojection Method for Raster Datasets of Continental to Global Extent

“…Lastly, a parallel algorithm design for spatially dependent map reprojections is challenging. Compared to reprojecting vector datasets, which are based on the point-by-point coordinate transformation between input and output datasets, reprojecting raster datasets involve conditions where multiple input raster cells are aggregated and/or resampled from smaller components to calculate pixel values and generate output datasets [4,21]. Because of the spatial dependency in data aggregation, input data are difficult to split among multiple processors.…”

“…Map reprojections for raster datasets have been studied for several decades (e.g., [4,[26][27][28]). Despite various ways of performing reprojections, we refer to the accurate raster reprojection technique presented in [4].…”

“…Despite various ways of performing reprojections, we refer to the accurate raster reprojection technique presented in [4]. Compared to map reprojection methods based on the center point of a pixel and treated as a vector map reprojection (with only nearest neighbor resampling), this cited method computes the four corner points of a pixel and allows various options for resampling the resulting pixel value.…”

“…Our algorithm is based on the rigorous map reprojection method that uses the four-corner technique for calculating pixel values and further utilizes both forward and inverse projection processes [4]. This method yields more accurate results, especially for images of global coverage even though it is more compute-intensive compared to map reprojection methods based on approximation [4]. Our algorithm introduces two key strategies for handling the data.…”

Interactive video maps: A year in the life of Earth's CO2

“…Because a smoothly animated movie requires 25 or more frames per second, a considerable amount of raster imagery needs to be processed. The traditional CPU-based approach for projecting raster images with continuous tone colors, as described by Finn et al (2012b), is too slow. Attempts at accelerating raster projections have been presented by various authors (Finn et al, 2012a;Tang & Feng, in press;Xie, Tang, Sun, & Chen, 2011;Zhao, Cheng, Dong, Fang, & Li, 2011), but the required technology is often not available in web browsers, geometrical accuracy is sacrificed for speed, or access to cloud computing may be needed (see Jenny, Šavrič, & Liem, 2015 for details).…”