Generalization
Generalization
part of the Multiscale representation theme from CEGIS
To display content legibly within the small space of a topographic map at a specific map scale, symbols must often be generalized. This means that the amount of information and detail shown must be reduced. This is particularly important for smaller-scale maps, which cover larger areas.
Generalization involves a variety of techniques for selecting the appropriate content for a given scale and symbolizing that content effectively.
The goal of generalization
Geospatial data are accessed, displayed, and used at various scales.
Traditional cartographic generalization aims to preserve the positional accuracy and shape characteristics of the original data, highlighting important physical features. These features can vary depending on the map's intended use and scale. However, generalization research has largely overlooked how the generalized data can be used in other applications.
Technological advances now allow for more detailed data collection—new sensors capture data more frequently and in greater detail than before. Modern technologies also provide enhanced storage, display, and processing capabilities, allowing for the efficient handling of large volumes of geospatial data.
In addition to improving generalization approaches, research and applications should focus on providing more realistic representations of topographic features across different scales. This enables more accurate analysis at any desired scale, which is particularly crucial for time-sensitive applications, such as flood forecasting.
The goal of generalization research at CEGIS is to develop and test automated methods that can generalize data from highly detailed to more generalized scales, tailored for various mapping and modeling needs.
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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At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a...AuthorsLarry Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. ShaversGeneralization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
Large geospatial datasets must often be generalized for analysis and display at reduced scales. Automated methods including artificial intelligence and deep learning are being applied to this problem, but the results are often analyzed on the basis of limited and subjective measures. To better support automation, a project is underway to develop a robust Python toolkit for computing...AuthorsBarry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Ethan J. ShaversGeoAI 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. VarankaWatersheds and drainage networks
This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the...AuthorsLarry Stanislawski, Ethan J. ShaversThe 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure
The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data...AuthorsBarbara P. Buttenfield, Larry Stanislawski, Barry J. Kronenfeld, Ethan J. Shavers
CEGIS science themes
Theme topics home
Multiscale representation
Cartographic representation
Feature representation
Generalization
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 Generalization publications
All Multiscale representation publications
All CEGIS publications
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
GeoAI in the US Geological Survey for topographic mapping
Watersheds and drainage networks
The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure
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 display content legibly within the small space of a topographic map at a specific map scale, symbols must often be generalized. This means that the amount of information and detail shown must be reduced. This is particularly important for smaller-scale maps, which cover larger areas.
Generalization involves a variety of techniques for selecting the appropriate content for a given scale and symbolizing that content effectively.
The goal of generalization
Geospatial data are accessed, displayed, and used at various scales.
Traditional cartographic generalization aims to preserve the positional accuracy and shape characteristics of the original data, highlighting important physical features. These features can vary depending on the map's intended use and scale. However, generalization research has largely overlooked how the generalized data can be used in other applications.
Technological advances now allow for more detailed data collection—new sensors capture data more frequently and in greater detail than before. Modern technologies also provide enhanced storage, display, and processing capabilities, allowing for the efficient handling of large volumes of geospatial data.
In addition to improving generalization approaches, research and applications should focus on providing more realistic representations of topographic features across different scales. This enables more accurate analysis at any desired scale, which is particularly crucial for time-sensitive applications, such as flood forecasting.
The goal of generalization research at CEGIS is to develop and test automated methods that can generalize data from highly detailed to more generalized scales, tailored for various mapping and modeling needs.
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!
-
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a...AuthorsLarry Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. ShaversGeneralization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
Large geospatial datasets must often be generalized for analysis and display at reduced scales. Automated methods including artificial intelligence and deep learning are being applied to this problem, but the results are often analyzed on the basis of limited and subjective measures. To better support automation, a project is underway to develop a robust Python toolkit for computing...AuthorsBarry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Ethan J. ShaversGeoAI 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. VarankaWatersheds and drainage networks
This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the...AuthorsLarry Stanislawski, Ethan J. ShaversThe 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure
The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data...AuthorsBarbara P. Buttenfield, Larry Stanislawski, Barry J. Kronenfeld, Ethan J. Shavers
CEGIS science themes
Theme topics home
Multiscale representation
Cartographic representation
Feature representation
Generalization
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 Generalization publications
All Multiscale representation publications
All CEGIS publications
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
GeoAI in the US Geological Survey for topographic mapping
Watersheds and drainage networks
The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
