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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

Collage of software mapper tools.

2026 18th CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) is proud to host the 2026 18th CEGIS Annual Research Meeting June 23-25, 2026, in Denver, Colorado. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our...
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), 3D Elevation Program, 3D Hydrography Program, 3D National Topography Model, National Geospatial Technical Operations Center
2026 18th CEGIS Annual Research Meeting

2026 18th CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) is proud to host the 2026 18th CEGIS Annual Research Meeting June 23-25, 2026, in Denver, Colorado. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our...
Learn More
Collage of software mapper tools.

2025 CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) was proud to host the 2025 CEGIS Annual Research Meeting July 22-24, 2025. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our researchers and their...
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), 3D Elevation Program, 3D Hydrography Program, 3D National Topography Model, National Geospatial Technical Operations Center
2025 CEGIS Annual Research Meeting

2025 CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) was proud to host the 2025 CEGIS Annual Research Meeting July 22-24, 2025. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our researchers and their...
Learn More
USGS

GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

A reusable Python workflow for the standardized retrieval and alignment of geospatial data
By
Community for Data Integration (CDI)
GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

A reusable Python workflow for the standardized retrieval and alignment of geospatial data
Learn More
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)
Modeling surface water

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

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

Scale Specific Sinuosity metrics and associated derived and modeled Manning’s n values for the global SWOT River Database (SWORD), v17b Scale Specific Sinuosity metrics and associated derived and modeled Manning’s n values for the global SWOT River Database (SWORD), v17b

Scale Specific Sinuosity (S3) attributes are generated for the global SWOT River Database (SWORD) stream reaches greater than 150 meters in length. The S3 and SWORD reach attributes are used in a Random Forest model, trained on 745 gage sites on SWORD measured reaches in North America with available USGS ADCP data. The model is used to predict a baseline Manning’s n resistance...
By
National Geospatial Program

Baseline Flow Resistance Parameters for Remote Sensing River Discharge (RSQ) from River Morphology Characteristics in the United States Baseline Flow Resistance Parameters for Remote Sensing River Discharge (RSQ) from River Morphology Characteristics in the United States

This dataset contains discharge measurements derived from acoustic Doppler current profiler (ADCP) observations collected at U.S. Geological Survey (USGS) stream gage locations that are observable by the Surface Water and Ocean Topography (SWOT) satellite. ADCP measurements were compiled for the period 2015 through December 1, 2025 and paired with reach-scale median SWOT-observed water...
By
Water Resources Mission Area
Filter Total Items: 31

Evaluating the reliability of U.S. Geological Survey Three Dimensional Elevation Program lidar-derived building heights using multi-source comparison Evaluating the reliability of U.S. Geological Survey Three Dimensional Elevation Program lidar-derived building heights using multi-source comparison

No abstract available.
Authors
Jung-Kuan Liu, Ethan J. Shavers, Samantha T. Arundel, Rongjun Qin
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Estimating baseline flow resistance parameters for remote sensing river discharge (RSQ) from river morphology characteristics Estimating baseline flow resistance parameters for remote sensing river discharge (RSQ) from river morphology characteristics

This study demonstrates that flow resistance in rivers, quantified via calibration of a modified Manning equation, can be estimated from channel hydrometric information available from globally derived data sets for a subset of river reaches in Alaska and with less certainty in a subset of river reaches in the contiguous United States. The channel morphometry data sets tested include the...
Authors
David M. Bjerklie, Merritt Elizabeth Harlan, Ethan J. Shavers, Craig B. Brinkerhoff, Lawrence V. Stanislawski, J. Toby Minear, Robert W. Dudley
By
New England Water Science Center

Evaluating U.S. Geological Survey 3D Elevation Program LiDAR data as a source for building height Evaluating U.S. Geological Survey 3D Elevation Program LiDAR data as a source for building height

Accurate building height information is used for numerous urban applications, including infrastructure modeling, disaster risk assessment, and energy demand estimation. The U.S. Geological Survey collects airborne lidar data to support the three-dimensional elevation program (3DEP). The high point density and vertical accuracy of the lidar point clouds are optimal for 3D mapping. Few...
Authors
Jung-Kuan Liu, Ethan J. Shavers, Samantha T. Arundel, Rongjun Qin
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Structure-aware unsupervised transmission corridor structure extraction from 3DEP lidar using graph attention embeddings and reinforcement-learned DBSCAN Structure-aware unsupervised transmission corridor structure extraction from 3DEP lidar using graph attention embeddings and reinforcement-learned DBSCAN

Transmission corridors are critical components of power delivery infrastructure, and their large-area monitoring increasingly relies on national-scale airborne lidar programs. This study presents an unsupervised, scalable framework for extracting transmission corridor structures (including conductors and supporting towers) from U.S. Geological Survey (USGS) 3D Elevation Program (3DEP)...
Authors
Nanzhou Hu, Zhe Zhang, Samantha T. Arundel, Ethan J. Shavers, Jung-Kuan Liu
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

The effects of line simplification on planform geometry The effects of line simplification on planform geometry

Data on maps should retain accuracy regardless of scale. Yet, as cartographic lines are generalized, there can be impacts on properties such as topology, density, and planform geometry. Here, we investigate the use of the Scale Specific Sinuosity (S3) metric (Stanislawski et al., 2023) to evaluate the effects of line simplification on planform geometry, which is the bends of streams in...
Authors
Ethan J. Shavers, Lawrence V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield
By
Core Science Systems Mission Area, National Geospatial Program, National Geospatial Technical Operations Center

Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning

Monitoring the extent of surface water features (hydrography), accurately storing them in databases, and representing them on topographic maps are essential for various applications such as navigation and policy-making for legislative boundaries and permitting. In this context, hydrographic data includes features that generally have water present or image data showing signs that water is...
Authors
Lawrence V. Stanislawski, Rongjun Qin, Jung-Kuan Liu, Ethan J. Shavers, Shaowen Wang, Nattapon Jaroenchai, Philip T. Thiem
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Assessment of density pattern retention of generalized data for 1:100,000-scale United States topographic maps Assessment of density pattern retention of generalized data for 1:100,000-scale United States topographic maps

Cartographic generalization reduces the complexity of geographic data to produce legible, smaller-scale displays that retain essential information and logical geographic patterns. Generalization is a vital process in topographic map production. An important challenge in this process is managing and evaluating consistency across scale in the density and spatial distribution of map...
Authors
Lawrence V. Stanislawski, Bethany Ann Walker, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Transfer learning with convolutional neural networks for hydrological streamline delineation 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, Lawrence 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 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 Liu, Samantha T. Arundel, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means

The United States Geological Survey (USGS) coordinates the collection of hydrographic features derived from remotely sensed interferometric synthetic aperture radar (IfSAR) elevation and intensity data in Alaska. Hydrographic features are cartographic representations of surface water features such as stream, rivers, lakes, ponds, canals, etc. Collection and validation procedures involve...
Authors
Lawrence V. Stanislawski, Ethan J. Shavers, Neal J. Pastick, Philip T. Thiem, Shaowen Wang, Nattapon Jaroenchai, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Untangling the knots: A procedure for identifying discernibility conflicts on a cartographic line Untangling the knots: A procedure for identifying discernibility conflicts on a cartographic line

Reducing detail on polyline features aids in legibility, allowing features to appear more distinct and preventing coalescence with other features. Current metrics for evaluating generalization outcomes emphasize geometric change rather than legibility. The present study reports on development and testing of a vector-based metric of the discernibility of a single polyline feature or group...
Authors
Barry J. Kronenfeld, Barbara P. Buttenfield, Lawrence V. Stanislawski, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks 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, Lawrence V. Stanislawski
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

Collage of software mapper tools.

2026 18th CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) is proud to host the 2026 18th CEGIS Annual Research Meeting June 23-25, 2026, in Denver, Colorado. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our...
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), 3D Elevation Program, 3D Hydrography Program, 3D National Topography Model, National Geospatial Technical Operations Center
2026 18th CEGIS Annual Research Meeting

2026 18th CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) is proud to host the 2026 18th CEGIS Annual Research Meeting June 23-25, 2026, in Denver, Colorado. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our...
Learn More
Collage of software mapper tools.

2025 CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) was proud to host the 2025 CEGIS Annual Research Meeting July 22-24, 2025. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our researchers and their...
By
Core Science Systems Mission Area, National Geospatial Program, Center of Excellence for Geospatial Information Science (CEGIS), 3D Elevation Program, 3D Hydrography Program, 3D National Topography Model, National Geospatial Technical Operations Center
2025 CEGIS Annual Research Meeting

2025 CEGIS Annual Research Meeting

The Center of Excellence for Geospatial Information Science (CEGIS) was proud to host the 2025 CEGIS Annual Research Meeting July 22-24, 2025. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our researchers and their...
Learn More
USGS

GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

A reusable Python workflow for the standardized retrieval and alignment of geospatial data
By
Community for Data Integration (CDI)
GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

GeoDRAW (Geospatial Data Retrieval and Alignment Workflow) to advance data-visualization and earth systems model development

A reusable Python workflow for the standardized retrieval and alignment of geospatial data
Learn More
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)
Modeling surface water

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

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

Scale Specific Sinuosity metrics and associated derived and modeled Manning’s n values for the global SWOT River Database (SWORD), v17b Scale Specific Sinuosity metrics and associated derived and modeled Manning’s n values for the global SWOT River Database (SWORD), v17b

Scale Specific Sinuosity (S3) attributes are generated for the global SWOT River Database (SWORD) stream reaches greater than 150 meters in length. The S3 and SWORD reach attributes are used in a Random Forest model, trained on 745 gage sites on SWORD measured reaches in North America with available USGS ADCP data. The model is used to predict a baseline Manning’s n resistance...
By
National Geospatial Program

Baseline Flow Resistance Parameters for Remote Sensing River Discharge (RSQ) from River Morphology Characteristics in the United States Baseline Flow Resistance Parameters for Remote Sensing River Discharge (RSQ) from River Morphology Characteristics in the United States

This dataset contains discharge measurements derived from acoustic Doppler current profiler (ADCP) observations collected at U.S. Geological Survey (USGS) stream gage locations that are observable by the Surface Water and Ocean Topography (SWOT) satellite. ADCP measurements were compiled for the period 2015 through December 1, 2025 and paired with reach-scale median SWOT-observed water...
By
Water Resources Mission Area
Filter Total Items: 31

Evaluating the reliability of U.S. Geological Survey Three Dimensional Elevation Program lidar-derived building heights using multi-source comparison Evaluating the reliability of U.S. Geological Survey Three Dimensional Elevation Program lidar-derived building heights using multi-source comparison

No abstract available.
Authors
Jung-Kuan Liu, Ethan J. Shavers, Samantha T. Arundel, Rongjun Qin
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Estimating baseline flow resistance parameters for remote sensing river discharge (RSQ) from river morphology characteristics Estimating baseline flow resistance parameters for remote sensing river discharge (RSQ) from river morphology characteristics

This study demonstrates that flow resistance in rivers, quantified via calibration of a modified Manning equation, can be estimated from channel hydrometric information available from globally derived data sets for a subset of river reaches in Alaska and with less certainty in a subset of river reaches in the contiguous United States. The channel morphometry data sets tested include the...
Authors
David M. Bjerklie, Merritt Elizabeth Harlan, Ethan J. Shavers, Craig B. Brinkerhoff, Lawrence V. Stanislawski, J. Toby Minear, Robert W. Dudley
By
New England Water Science Center

Evaluating U.S. Geological Survey 3D Elevation Program LiDAR data as a source for building height Evaluating U.S. Geological Survey 3D Elevation Program LiDAR data as a source for building height

Accurate building height information is used for numerous urban applications, including infrastructure modeling, disaster risk assessment, and energy demand estimation. The U.S. Geological Survey collects airborne lidar data to support the three-dimensional elevation program (3DEP). The high point density and vertical accuracy of the lidar point clouds are optimal for 3D mapping. Few...
Authors
Jung-Kuan Liu, Ethan J. Shavers, Samantha T. Arundel, Rongjun Qin
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Structure-aware unsupervised transmission corridor structure extraction from 3DEP lidar using graph attention embeddings and reinforcement-learned DBSCAN Structure-aware unsupervised transmission corridor structure extraction from 3DEP lidar using graph attention embeddings and reinforcement-learned DBSCAN

Transmission corridors are critical components of power delivery infrastructure, and their large-area monitoring increasingly relies on national-scale airborne lidar programs. This study presents an unsupervised, scalable framework for extracting transmission corridor structures (including conductors and supporting towers) from U.S. Geological Survey (USGS) 3D Elevation Program (3DEP)...
Authors
Nanzhou Hu, Zhe Zhang, Samantha T. Arundel, Ethan J. Shavers, Jung-Kuan Liu
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

The effects of line simplification on planform geometry The effects of line simplification on planform geometry

Data on maps should retain accuracy regardless of scale. Yet, as cartographic lines are generalized, there can be impacts on properties such as topology, density, and planform geometry. Here, we investigate the use of the Scale Specific Sinuosity (S3) metric (Stanislawski et al., 2023) to evaluate the effects of line simplification on planform geometry, which is the bends of streams in...
Authors
Ethan J. Shavers, Lawrence V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield
By
Core Science Systems Mission Area, National Geospatial Program, National Geospatial Technical Operations Center

Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning Multi-temporal surface water mapping with high-resolution elevation and image data through weakly supervised deep learning

Monitoring the extent of surface water features (hydrography), accurately storing them in databases, and representing them on topographic maps are essential for various applications such as navigation and policy-making for legislative boundaries and permitting. In this context, hydrographic data includes features that generally have water present or image data showing signs that water is...
Authors
Lawrence V. Stanislawski, Rongjun Qin, Jung-Kuan Liu, Ethan J. Shavers, Shaowen Wang, Nattapon Jaroenchai, Philip T. Thiem
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Assessment of density pattern retention of generalized data for 1:100,000-scale United States topographic maps Assessment of density pattern retention of generalized data for 1:100,000-scale United States topographic maps

Cartographic generalization reduces the complexity of geographic data to produce legible, smaller-scale displays that retain essential information and logical geographic patterns. Generalization is a vital process in topographic map production. An important challenge in this process is managing and evaluating consistency across scale in the density and spatial distribution of map...
Authors
Lawrence V. Stanislawski, Bethany Ann Walker, Barry J. Kronenfeld, Barbara P. Buttenfield, Ethan J. Shavers, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Transfer learning with convolutional neural networks for hydrological streamline delineation 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, Lawrence 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 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 Liu, Samantha T. Arundel, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means Adaptive fine-tuning for transferring a U-net hydrography extraction model using K-means

The United States Geological Survey (USGS) coordinates the collection of hydrographic features derived from remotely sensed interferometric synthetic aperture radar (IfSAR) elevation and intensity data in Alaska. Hydrographic features are cartographic representations of surface water features such as stream, rivers, lakes, ponds, canals, etc. Collection and validation procedures involve...
Authors
Lawrence V. Stanislawski, Ethan J. Shavers, Neal J. Pastick, Philip T. Thiem, Shaowen Wang, Nattapon Jaroenchai, Zhe Jiang, Barry J. Kronenfeld, Barbara P. Buttenfield, Adam Camerer
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Untangling the knots: A procedure for identifying discernibility conflicts on a cartographic line Untangling the knots: A procedure for identifying discernibility conflicts on a cartographic line

Reducing detail on polyline features aids in legibility, allowing features to appear more distinct and preventing coalescence with other features. Current metrics for evaluating generalization outcomes emphasize geometric change rather than legibility. The present study reports on development and testing of a vector-based metric of the discernibility of a single polyline feature or group...
Authors
Barry J. Kronenfeld, Barbara P. Buttenfield, Lawrence V. Stanislawski, Ethan J. Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS)

Unravelling spatial heterogeneity of inundation pattern domains for 2D analysis of fluvial landscapes and drainage networks 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, Lawrence V. Stanislawski
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.

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