Parallel systems
Parallel systems
part of the Parallel computing theme from CEGIS
To use and generate data quickly we need computer systems that enable that functionality.
One can employ virtual machines and isolated software environments (containers) in various ways to offer flexibility for users and system administrators.
GPU and similar hardware can provide additional computation resources for many tasks.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
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Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar...AuthorsJung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. ShaversA guide to creating an effective big data management framework
Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey...AuthorsSamantha Arundel, Kevin G McKeehan, Bryan B Campbell, Andrew N. Bulen, Philip T. ThiemHistorical maps inform landform cognition in machine learning
No abstract available.AuthorsSamantha Arundel, Sinha Gaurav, Wenwen Li, David P. Martin, Kevin G McKeehan, Philip T. ThiemScaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution...AuthorsLarry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. ButtenfieldDeep learning detection and recognition of spot elevations on historic topographic maps
Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to...AuthorsSamantha Arundel, Trenton P. Morgan, Philip T. Thiem
CEGIS science themes
Theme topics home
Parallel computing
Big data
Parallel software
Parallel systems
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
All Parallel systems publications
All Parallel computing publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
A guide to creating an effective big data management framework
Historical maps inform landform cognition in machine learning
Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
Deep learning detection and recognition of spot elevations on historic topographic maps
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri

Samantha T Arundel, PhD
Research Director
Senior Science Advisor
Ethan Shavers, PhD
CEGIS Section Chief/ Supervisory Geographer
Jung kuan (Ernie) Liu
Physical Research Scientist
To use and generate data quickly we need computer systems that enable that functionality.
One can employ virtual machines and isolated software environments (containers) in various ways to offer flexibility for users and system administrators.
GPU and similar hardware can provide additional computation resources for many tasks.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
-
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar...AuthorsJung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. ShaversA guide to creating an effective big data management framework
Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey...AuthorsSamantha Arundel, Kevin G McKeehan, Bryan B Campbell, Andrew N. Bulen, Philip T. ThiemHistorical maps inform landform cognition in machine learning
No abstract available.AuthorsSamantha Arundel, Sinha Gaurav, Wenwen Li, David P. Martin, Kevin G McKeehan, Philip T. ThiemScaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution...AuthorsLarry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. ButtenfieldDeep learning detection and recognition of spot elevations on historic topographic maps
Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to...AuthorsSamantha Arundel, Trenton P. Morgan, Philip T. Thiem
CEGIS science themes
Theme topics home
Parallel computing
Big data
Parallel software
Parallel systems
You will find here a sampling of publications. More are available and are being published throughout the year.
Check back often or view our custom search for more!
All Parallel systems publications
All Parallel computing publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
A guide to creating an effective big data management framework
Historical maps inform landform cognition in machine learning
Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
Deep learning detection and recognition of spot elevations on historic topographic maps
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
