A multilevel parallel and scalable single-host GPU cluster framework for large-scale geospatial data processing

Abstract

Geospatial data exists in a variety of formats, including rasters, vector data, and large-scale geospatial databases. There exists an ever-growing number of sensors that are collecting this data, resulting in the explosive growth and scale of high-resolution remote sensing geospatial data collections. A particularly challenging domain of geospatial data processing involves mining information from high resolution remote sensing imagery. The prevalence of high-resolution raster geospatial data collections represents a significant data challenge, as a single remote sensing image is composed of hundreds of millions of pixels. We have developed a robust application framework which exploits graphics processing unit (GPU) clusters to perform high-throughput geospatial data processing. We process geospatial raster data concurrently across tiles of large geospatial data rasters, utilizing GPU co-processors driven by CPU threads to extract refined geospatial information. The framework can produce output rasters or perform image information mining to write data into a geospatial database.