Gpu Utilization in Geoprocessing Big Geodata: A Review

Abstract

Abstract. The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

Challenges of a GIS-based physical-geographical regionalization of Poland

“…Even though every single operation was limited to only one CPU thread, the load was always only up to 50-60% of its potential computing power, and only one-third of the accessible RAM was permanently in use. Therefore, better-optimized GIS tools, including native implementation of parallel computations and support for hardware acceleration, e.g., general-purpose computing on graphics processing units (GPGPU) technology (Zhang & You, 2012;Stojanovic & Stojanovic, 2013Prasad et al, 2015;Zhou et al, 2017;Breunig et al, 2020;Saupi Teri et al, 2022), would be beneficial in future studies related to regionalization of the whole country. Nevertheless, findings resulting from the microregionalization of the Greater Poland Voivodeship (Piniarski, 2020) could be a reasonable baseline for preparing all the necessary spatial datasets and successfully implementing the developed GIS procedure for creating a microregional division of all the Polish provinces and, finally, the whole country area.…”

Earth Observation Datacubes Multi-Visualization Toolbox

“…A more modern way to tackle it is to use GPUs instead of standard CPU processing. When reviewing the use of GPUs to process geospatial data, the emphasis is often put on the parallel processing of big geospatial datasets but not on their visualization (Saupi Teri et al, 2022). The next necessary step is the visualization of geospatial data using GPU resources.…”