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

@article{Scott2014AMP,
  title={A multilevel parallel and scalable single-host GPU cluster framework for large-scale geospatial data processing},
  author={Grant J. Scott and Kirk Backus and Derek Anderson},
  journal={2014 IEEE Geoscience and Remote Sensing Symposium},
  year={2014},
  pages={2475-2478}
}
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… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-8 OF 8 REFERENCES

Highly-Parallel GPU Architecture for Lossy Hyperspectral Image Compression

  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2013
VIEW 1 EXCERPT

Dynamic load balancing on GPU clusters for large-scale K-Means clustering

  • 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE)
  • 2012
VIEW 1 EXCERPT

High performance image processing of satellite images using graphics processing units

  • 2011 IEEE International Geoscience and Remote Sensing Symposium
  • 2011
VIEW 1 EXCERPT

The GPU Enhanced Parallel Computing for Large Scale Data Clustering

  • 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
  • 2011
VIEW 1 EXCERPT

Real-time Minute Change Detection on GPU for Cellular and Remote Sensor Imaging

  • 2009 International Conference on Advanced Information Networking and Applications Workshops
  • 2009
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…