Remote sensing
Remote sensing
part of the Hydrography theme from CEGIS
From small cameras to big satellites, remote sensing involves using sensors to measure things from a distance and is an important tool for mapping.
The large breadth of the United States makes measuring and mapping the ever-changing surface water a huge challenge.
Remote sensing allows us to understand where and when water is collecting on the earth surface.
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. 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 CamererScaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution...AuthorsLarry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. ButtenfieldWeakly 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 UseryGeoAI 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
Hydrography
Cartographic representation
Modeling surface water
Remote sensing
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 Remote sensing publications
All Hydrography publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
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
From small cameras to big satellites, remote sensing involves using sensors to measure things from a distance and is an important tool for mapping.
The large breadth of the United States makes measuring and mapping the ever-changing surface water a huge challenge.
Remote sensing allows us to understand where and when water is collecting on the earth surface.
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. 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 CamererScaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution...AuthorsLarry Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. ButtenfieldWeakly 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 UseryGeoAI 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
Hydrography
Cartographic representation
Modeling surface water
Remote sensing
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 Remote sensing publications
All Hydrography publications
All CEGIS publications
Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds
Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
GeoAI in the US Geological Survey for topographic mapping
CEGIS - Denver, Colorado

CEGIS - Rolla, Missouri
