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
Filter Total Items: 25
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels 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 Stanislawski, Ethan J. Shavers, E. Lynn Usery
GeoAI in the US Geological Survey for topographic mapping 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 Stanislawski, Philip T. Thiem, Dalia E. Varanka
Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling 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
Watersheds and drainage networks 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...
Authors
Larry Stanislawski, Ethan J. Shavers
The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure 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...
Authors
Barbara P. Buttenfield, Larry Stanislawski, Barry J. Kronenfeld, Ethan J. Shavers
An attention U-Net model for detection of fine-scale hydrologic streamlines 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...
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
Channel cross-section analysis for automated stream head identification Channel cross-section analysis for automated stream head identification
Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics...
Authors
Ethan J. Shavers, Larry Stanislawski
Preserving meander bend geometry through scale Preserving meander bend geometry through scale
Stream meander geometry is a function of hydrologic, geologic, and anthropogenic forces. Meander morphometrics are used in geomorphic classification, ecological characterization, and tectonic and hydrologic change detection. Thus, detailed measurement and classification of meander geometry is imperative to multiscale representation of hydrographic features, which raises important...
Authors
Ethan J. Shavers, Larry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld
OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features
The National Hydrography Dataset (NHD) is a foundational geospatial data source in the United States that enables extensive and diverse environmental research and supports decision-making in numerous contexts. However, the NHD requires regular validation and update given possible inconsistent initial collection and hydrographic changes. Furthermore, systems or tools that use NHD data...
Authors
Ting Li, Larry Stanislawski, Tyler Brockmeyer, Shaowen Wang, Ethan J. Shavers
Scale-specific metrics for adaptive generalization and geomorphic classification of stream features Scale-specific metrics for adaptive generalization and geomorphic classification of stream features
The Richardson plot has been used to illustrate fractal dimension of naturally occurring landscape features that are sensitive to changes in scale or resolution, such as coastlines and river channels. The Richardson method estimates the length of a path by traversing (i.e., “walking”) the path with a specific stride length. Fractal dimension is determined as the slope of the Richardson...
Authors
Larry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld, Ethan J. Shavers
Streams do work: Measuring the work of low-order streams on the landscape using point clouds Streams do work: Measuring the work of low-order streams on the landscape using point clouds
The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for...
Authors
Ethan J. Shavers, Larry V. Stanislawski
Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning
High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such...
Authors
Larry Stanislawski, Tyler Brockmeyer, Ethan J. Shavers
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
Filter Total Items: 25
Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels 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 Stanislawski, Ethan J. Shavers, E. Lynn Usery
GeoAI in the US Geological Survey for topographic mapping 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 Stanislawski, Philip T. Thiem, Dalia E. Varanka
Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling 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
Watersheds and drainage networks 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...
Authors
Larry Stanislawski, Ethan J. Shavers
The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure 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...
Authors
Barbara P. Buttenfield, Larry Stanislawski, Barry J. Kronenfeld, Ethan J. Shavers
An attention U-Net model for detection of fine-scale hydrologic streamlines 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...
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
Channel cross-section analysis for automated stream head identification Channel cross-section analysis for automated stream head identification
Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics...
Authors
Ethan J. Shavers, Larry Stanislawski
Preserving meander bend geometry through scale Preserving meander bend geometry through scale
Stream meander geometry is a function of hydrologic, geologic, and anthropogenic forces. Meander morphometrics are used in geomorphic classification, ecological characterization, and tectonic and hydrologic change detection. Thus, detailed measurement and classification of meander geometry is imperative to multiscale representation of hydrographic features, which raises important...
Authors
Ethan J. Shavers, Larry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld
OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features
The National Hydrography Dataset (NHD) is a foundational geospatial data source in the United States that enables extensive and diverse environmental research and supports decision-making in numerous contexts. However, the NHD requires regular validation and update given possible inconsistent initial collection and hydrographic changes. Furthermore, systems or tools that use NHD data...
Authors
Ting Li, Larry Stanislawski, Tyler Brockmeyer, Shaowen Wang, Ethan J. Shavers
Scale-specific metrics for adaptive generalization and geomorphic classification of stream features Scale-specific metrics for adaptive generalization and geomorphic classification of stream features
The Richardson plot has been used to illustrate fractal dimension of naturally occurring landscape features that are sensitive to changes in scale or resolution, such as coastlines and river channels. The Richardson method estimates the length of a path by traversing (i.e., “walking”) the path with a specific stride length. Fractal dimension is determined as the slope of the Richardson...
Authors
Larry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld, Ethan J. Shavers
Streams do work: Measuring the work of low-order streams on the landscape using point clouds Streams do work: Measuring the work of low-order streams on the landscape using point clouds
The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for...
Authors
Ethan J. Shavers, Larry V. Stanislawski
Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning
High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such...
Authors
Larry Stanislawski, Tyler Brockmeyer, Ethan J. Shavers
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.