A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures
As an important spatiotemporal simulation approach and an effective tool for develo** and
examining spatial optimization strategies (eg, land allocation and planning), geospatial …
examining spatial optimization strategies (eg, land allocation and planning), geospatial …
A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data
Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In
many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over …
many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over …
A parallel computing approach to spatial neighboring analysis of large amounts of terrain data using spark
J Zhang, Z Ye, K Zheng - Sensors, 2021 - mdpi.com
Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the
big data era, high-resolution raster data offer us abundant and valuable information, and …
big data era, high-resolution raster data offer us abundant and valuable information, and …
Parallel cartographic modeling: a methodology for parallelizing spatial data processing
This article establishes a new methodological framework for parallelizing spatial data
processing called parallel cartographic modeling, which extends the widely adopted …
processing called parallel cartographic modeling, which extends the widely adopted …
PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil map**
Digital soil map** (DSM) at high spatial resolutions over large areas often demands
considerable computing power. This study aims to harness the heterogeneous computing …
considerable computing power. This study aims to harness the heterogeneous computing …
A two-level storage strategy for map-reduce enabled computation of local map algebra
J Zhang, S Zhou, T Liang, Y Li, C Chen, H **a - Earth Science Informatics, 2020 - Springer
In the big data era, high-resolution raster-based geocomputation has been widely employed
in geospatial studies. The algorithms used in local map algebra operations are data …
in geospatial studies. The algorithms used in local map algebra operations are data …
Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth …
Digital earth science data originated from sensors aboard satellites and platforms such as
airplane, UAV, and mobile systems are increasingly available with high spectral, spatial …
airplane, UAV, and mobile systems are increasingly available with high spectral, spatial …
Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi
F Huang, H Yang, J Tao, J Wang… - … : Practice and Experience, 2021 - Wiley Online Library
In unmanned aerial vehicle (UAV) image‐processing applications, one needs to implement
different parallel image‐enhancement algorithms on several high‐performance computing …
different parallel image‐enhancement algorithms on several high‐performance computing …
Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA …
High spectral, spatial, vertical and temporal resolution data are increasingly available and
result in the serious challenge to process big remote-sensing images effectively and …
result in the serious challenge to process big remote-sensing images effectively and …
The Architecture of High-Performance GIS
The focus of existing GIS is either on data management or on algorithm performance. There
is a lack of comprehensive solutions for data management, geographic computing, and …
is a lack of comprehensive solutions for data management, geographic computing, and …