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Ethan Shavers, PhD

Dr. Ethan Shavers is the CEGIS Section Chief and lead researcher on the Remote Sensing and Modeling for Hydrography project. The project research focusses on developing strategies for automated hydrographic feature extraction and landscape characterization.

Dr. Shavers has a BS in Geology and PhD in Environmental Science and GIS, both from Saint Louis University (SLU). His dissertation work focused on remote sensing and spectral analysis of igneous lithologies. His work in the SLU Remote Sensing Lab involved optical instrument integration for unmanned aerial systems and remote sensing applications for precision agriculture. His federal career began as a Mendenhall Fellow in the CEGIS Multi-scale Representation project testing strategies for mapping headwater streams.

Science and Products

link
NHD, Elevation-derived hydrography drainage network in the Marshyhope Creek watershed, Maryland

Modeling surface water

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.
By
Center of Excellence for Geospatial Information Science (CEGIS)
link

Modeling surface water

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.
Learn More
link
USGS

Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data

This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
By
Community for Data Integration (CDI)
link

Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data

This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
Learn More
Filter Total Items: 22

Transfer learning with convolutional neural networks for hydrological streamline delineation

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline...
Authors
Nattapon Jaroenchai, Shaowen Wang, Larry V. Stanislawski, Ethan J. Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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...
Authors
Jung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks

Fluvial landscape analysis is an essential part of geomorphology, hydrology, ecology, and cartography. It is traditionally focused on the transition between hillslopes and channel domain, in which the network drainage is represented by static flow lines. However, the natural fluctuations of the processes occurring in the watershed induce lateral and longitudinal expansions and...
Authors
Pierfranco Costabile, Carmelina Costanzo, Margherita Lombardo, Ethan J. Shavers, Larry V. Stanislawski
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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...
Authors
Larry V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Generalization 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...
Authors
Barry J. Kronenfeld, Larry V. Stanislawski, Barbara P. Buttenfield, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Transferring 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...
Authors
Larry V. Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Zhe Jiang, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Automated mapping of culverts, bridges, and dams

Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in...
Authors
Ethan J. Shavers, Larry V. Stanislawski, Joel Schott, Zachary Brosseau
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance

No abstract available.
Authors
Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers, Larry V. Stanislawski
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Scaling-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...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Weakly 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...
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry V. Stanislawski, Ethan J. Shavers, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

GeoAI 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...
Authors
E. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry V. Stanislawski, Philip T. Thiem, Dalia E. Varanka
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy, Joel Schott
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Non-USGS Publications**

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

Science and Products

link
NHD, Elevation-derived hydrography drainage network in the Marshyhope Creek watershed, Maryland

Modeling surface water

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.
By
Center of Excellence for Geospatial Information Science (CEGIS)
link

Modeling surface water

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.
Learn More
link
USGS

Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data

This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
By
Community for Data Integration (CDI)
link

Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data

This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
Learn More
Filter Total Items: 22

Transfer learning with convolutional neural networks for hydrological streamline delineation

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline...
Authors
Nattapon Jaroenchai, Shaowen Wang, Larry V. Stanislawski, Ethan J. Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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...
Authors
Jung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks

Fluvial landscape analysis is an essential part of geomorphology, hydrology, ecology, and cartography. It is traditionally focused on the transition between hillslopes and channel domain, in which the network drainage is represented by static flow lines. However, the natural fluctuations of the processes occurring in the watershed induce lateral and longitudinal expansions and...
Authors
Pierfranco Costabile, Carmelina Costanzo, Margherita Lombardo, Ethan J. Shavers, Larry V. Stanislawski
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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...
Authors
Larry V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Generalization 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...
Authors
Barry J. Kronenfeld, Larry V. Stanislawski, Barbara P. Buttenfield, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Transferring 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...
Authors
Larry V. Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Zhe Jiang, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Automated mapping of culverts, bridges, and dams

Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in...
Authors
Ethan J. Shavers, Larry V. Stanislawski, Joel Schott, Zachary Brosseau
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Comparing line feature morphology with scale specific sinuosity distributions: A modified earth mover’s distance

No abstract available.
Authors
Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers, Larry V. Stanislawski
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Scaling-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...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Alexander Duffy, Philip T. Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Weakly 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...
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry V. Stanislawski, Ethan J. Shavers, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

GeoAI 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...
Authors
E. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry V. Stanislawski, Philip T. Thiem, Dalia E. Varanka
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically...
Authors
Larry V. Stanislawski, Ethan J. Shavers, Shaowen Wang, Zhe Jiang, E. Lynn Usery, Evan Moak, Alexander Duffy, Joel Schott
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Non-USGS Publications**

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

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