The above image is a rendering of a lidar-derived digital surface model overlaying a digital elevation model of a forested stream channel in central Iowa. The vegetation makes optical identification of the presence of water in channels difficult.
Modeling surface water
Modeling surface water
part of the Hydrography theme from CEGIS
Modeling hydrology is the use of computers to simulate the behavior of water on earth.
Hydrologic modeling has been employed for a long time for things like predicting the risk of floods given a certain amount of rain or how building structures like bridges might impact stream flow.
Research is underway to adapt modeling tools for use in mapping streams.
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 GuAt 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. ShaversTransferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different...AuthorsLarry V. Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Zhe Jiang, Adam CamererComparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance
No abstract available.AuthorsBarry J. Kronenfeld, Barbara Buttenfield, Ethan J. Shavers, Larry StanislawskiWeakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...AuthorsZhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
CEGIS science themes
Theme topics home
Hydrography
Cartographic representation
Modeling surface water
Remote sensing
Validation of elevation-derived channels
You will find here a sampling of multimedia with a Modeling Surface Water topic. More are available and are being posted throughout the year.
Check back often or view our custom search for more!
The above image is a rendering of a lidar-derived digital surface model overlaying a digital elevation model of a forested stream channel in central Iowa. The vegetation makes optical identification of the presence of water in channels difficult.
The image is a rendering of a lidar-derived digital elevation model of a low relief stream channel and associated National Hydrography Dataset line features in central Iowa. The low topographic relief makes flow accumulation modeling of surface water difficult.
The image is a rendering of a lidar-derived digital elevation model of a low relief stream channel and associated National Hydrography Dataset line features in central Iowa. The low topographic relief makes flow accumulation modeling of surface water difficult.
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 Modeling Surface Water publications
All Hydrography publications
All CEGIS publications
GeoAI for spatial image processing
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
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
Modeling hydrology is the use of computers to simulate the behavior of water on earth.
Hydrologic modeling has been employed for a long time for things like predicting the risk of floods given a certain amount of rain or how building structures like bridges might impact stream flow.
Research is underway to adapt modeling tools for use in mapping streams.
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 GuAt 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. ShaversTransferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different...AuthorsLarry V. Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Zhe Jiang, Adam CamererComparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance
No abstract available.AuthorsBarry J. Kronenfeld, Barbara Buttenfield, Ethan J. Shavers, Larry StanislawskiWeakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...AuthorsZhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
CEGIS science themes
Theme topics home
Hydrography
Cartographic representation
Modeling surface water
Remote sensing
Validation of elevation-derived channels
You will find here a sampling of multimedia with a Modeling Surface Water topic. More are available and are being posted throughout the year.
Check back often or view our custom search for more!
The above image is a rendering of a lidar-derived digital surface model overlaying a digital elevation model of a forested stream channel in central Iowa. The vegetation makes optical identification of the presence of water in channels difficult.
The above image is a rendering of a lidar-derived digital surface model overlaying a digital elevation model of a forested stream channel in central Iowa. The vegetation makes optical identification of the presence of water in channels difficult.
The image is a rendering of a lidar-derived digital elevation model of a low relief stream channel and associated National Hydrography Dataset line features in central Iowa. The low topographic relief makes flow accumulation modeling of surface water difficult.
The image is a rendering of a lidar-derived digital elevation model of a low relief stream channel and associated National Hydrography Dataset line features in central Iowa. The low topographic relief makes flow accumulation modeling of surface water difficult.
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 Modeling Surface Water publications
All Hydrography publications
All CEGIS publications
GeoAI for spatial image processing
At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
