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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

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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: 19

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

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

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 hydrogeomorphic
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)

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 remote sensing
Authors
Ethan J. Shavers, Larry Stanislawski, Joel Schott, Zachary Brosseau
By
Core Science Systems Mission Area, National Geospatial Program, 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 Buttenfield, Ethan J. Shavers, Larry 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 elevation data
Authors
Larry 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 in earth s
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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 terrain featur
Authors
E. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
By
National Geospatial Program

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 extract s
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)

Watersheds 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 detailed
Authors
Larry Stanislawski, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundati
Authors
Zewei Xu, Shaowen Wang, Larry Stanislawski, Zhe Jiang, Nattapon Jaroenchai, Arpan Man Sainju, Ethan J. Shavers, E. Lynn Usery, Li Chen, Zhiyu Li, Bin Su
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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
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: 19

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

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

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 hydrogeomorphic
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)

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 remote sensing
Authors
Ethan J. Shavers, Larry Stanislawski, Joel Schott, Zachary Brosseau
By
Core Science Systems Mission Area, National Geospatial Program, 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 Buttenfield, Ethan J. Shavers, Larry 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 elevation data
Authors
Larry 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 in earth s
Authors
Zhe Jiang, Wenchong He, M. S. Kirby, Arpan Man Sainju, Shaowen Wang, Larry Stanislawski, Ethan J. Shavers, E. Lynn Usery
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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 terrain featur
Authors
E. Lynn Usery, Samantha Arundel, Ethan J. Shavers, Larry Stanislawski, Philip T. Thiem, Dalia E. Varanka
By
National Geospatial Program

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 extract s
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)

Watersheds 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 detailed
Authors
Larry Stanislawski, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundati
Authors
Zewei Xu, Shaowen Wang, Larry Stanislawski, Zhe Jiang, Nattapon Jaroenchai, Arpan Man Sainju, Ethan J. Shavers, E. Lynn Usery, Li Chen, Zhiyu Li, Bin Su
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

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.