Foundations
Foundations
part of the Artificial Intelligence (AI) theme from CEGIS
In the context of AI, foundations refer to the fundamental theories and principles that form the basis of artificial intelligence.
This includes concepts like algorithms, data structures, logic, and mathematics used to develop AI systems.
Foundations in AI are like the building blocks or basic ideas that help create artificial intelligence.
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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GeoAI 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 GuGeoImageNet: 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 HsuGeoAI and the future of spatial analytics
This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a...AuthorsWenwen Li, Samantha ArundelDeep 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. ThiemGeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...AuthorsE. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
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 Foundations publications
All Artificial intelligence publications
All CEGIS publications
GeoAI for spatial image processing
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
GeoAI and the future of spatial analytics
Deep learning detection and recognition of spot elevations on historic topographic maps
GeoAI in the US Geological Survey for topographic mapping
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
In the context of AI, foundations refer to the fundamental theories and principles that form the basis of artificial intelligence.
This includes concepts like algorithms, data structures, logic, and mathematics used to develop AI systems.
Foundations in AI are like the building blocks or basic ideas that help create artificial intelligence.
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!
-
GeoAI 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 GuGeoImageNet: 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 HsuGeoAI and the future of spatial analytics
This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a...AuthorsWenwen Li, Samantha ArundelDeep 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. ThiemGeoAI in the US Geological Survey for topographic mapping
Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in...AuthorsE. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
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 Foundations publications
All Artificial intelligence publications
All CEGIS publications
GeoAI for spatial image processing
GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning
GeoAI and the future of spatial analytics
Deep learning detection and recognition of spot elevations on historic topographic maps
GeoAI in the US Geological Survey for topographic mapping
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
