Machine learning (ML)
Machine learning (ML)
part of the Artificial Intelligence (AI) theme from CEGIS
Machine learning (ML) is a type of technology that uses algorithms to find patterns and make predictions based on examples, like recommending movies based on past preferences.
CEGIS uses machine learning to map terrain features and analyze landscapes, which helps with planning and protecting the environment.
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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Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban...AuthorsJung-Kuan (Ernie) Liu, Rongjun Qin, Shuang SongEvaluation 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. ShaversAssessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created...AuthorsJung-Kuan (Ernie) Liu, Rongjun Qin, Samantha ArundelRemote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and...AuthorsHessah Albanwan, Rongjun Qin, Jung-Kuan (Ernie) LiuGeoAI for spatial image processing
The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the...AuthorsSamantha Arundel, Kevin G McKeehan, Wenwen Li, Zhining Gu
CEGIS science themes
Theme topics home
Artificial Intelligence (AI)
Foundations
Machine Learning (ML)
Ontologies
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 Machine learning publications
All Artificial Intelligence publications
All CEGIS publications
Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
GeoAI for spatial image processing
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
Machine learning (ML) is a type of technology that uses algorithms to find patterns and make predictions based on examples, like recommending movies based on past preferences.
CEGIS uses machine learning to map terrain features and analyze landscapes, which helps with planning and protecting the environment.
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!
-
Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban...AuthorsJung-Kuan (Ernie) Liu, Rongjun Qin, Shuang SongEvaluation 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. ShaversAssessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created...AuthorsJung-Kuan (Ernie) Liu, Rongjun Qin, Samantha ArundelRemote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and...AuthorsHessah Albanwan, Rongjun Qin, Jung-Kuan (Ernie) LiuGeoAI for spatial image processing
The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the...AuthorsSamantha Arundel, Kevin G McKeehan, Wenwen Li, Zhining Gu
CEGIS science themes
Theme topics home
Artificial Intelligence (AI)
Foundations
Machine Learning (ML)
Ontologies
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 Machine learning publications
All Artificial Intelligence publications
All CEGIS publications
Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope
Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
GeoAI for spatial image processing
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
