Image processing
Image processing
part of the Terrain theme from CEGIS
Image processing helps us study and understand different types of landscapes. We use algorithms and software to analyze satellite and airborne images to make detailed maps of the land and its features.
By using automated techniques such as feature extraction and building of 3D models, image processing helps scientists learn more about the land and how it changes over time.
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. ShaversRemote 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 GuHistorical maps inform landform cognition in machine learning
No abstract available.AuthorsSamantha Arundel, Sinha Gaurav, Wenwen Li, David M. Martin, Kevin G McKeehan, Philip T. ThiemGeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the...AuthorsWenwen Li, Sizhe Wang, Samantha Arundel, Chia-Yu Hsu
CEGIS science themes
Theme topics home
Terrain
Feature mapping
Hydrography/Hypsography integration
Image processing
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 Image processing publications
All Terrain publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
GeoAI for spatial image processing
Historical maps inform landform cognition in machine learning
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
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
Image processing helps us study and understand different types of landscapes. We use algorithms and software to analyze satellite and airborne images to make detailed maps of the land and its features.
By using automated techniques such as feature extraction and building of 3D models, image processing helps scientists learn more about the land and how it changes over time.
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. ShaversRemote 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 GuHistorical maps inform landform cognition in machine learning
No abstract available.AuthorsSamantha Arundel, Sinha Gaurav, Wenwen Li, David M. Martin, Kevin G McKeehan, Philip T. ThiemGeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the...AuthorsWenwen Li, Sizhe Wang, Samantha Arundel, Chia-Yu Hsu
CEGIS science themes
Theme topics home
Terrain
Feature mapping
Hydrography/Hypsography integration
Image processing
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 Image processing publications
All Terrain publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
GeoAI for spatial image processing
Historical maps inform landform cognition in machine learning
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
