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Larry Stanislawski

Lawrence (Larry) V. Stanislawski is a Research Cartographer for the Center of Excellence for Geospatial Information Science (CEGIS). His work focuses on generalization and multiscale representation that support or enable automated mapping and science investigations using geospatial data, particularly the National Map datasets.

Larry received his B.S. in Forest Resources and Conservation and his M.S. in Forest Remote Sensing from the University of Florida. He continued studying in the Surveying and Mapping Program at the University of Florida and performed research on GIS data accuracy and on high precision surveying with Global Position Systems (GPS). 

Prior to his work with the U.S. Geological Survey, Larry worked in various geoscience research and consultant positions, and as a GIS developer with the Army Corps of Engineers in Jacksonville, Florida.

In 1998, he began as a GIS Developer with National Geospatial Technical Operations Center leading development of automated systems to build the high-resolution National Hydrography Dataset (NHD) with conflation of medium resolution NHD data. During this time, he also designed and taught a Geomatics course at Missouri University of Science and Technology. 

Larry began working as a CEGIS research scientist in 2011. Larry’s research includes machine learning and high-performance computing to extract, validate, and generalize hydrography and other features using high resolution elevation and remotely sensed data, such as lidar from the 3D Elevation Program.

 

Science and Products

Filter Total Items: 37

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, Larry Stanislawski, Ethan Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
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 Shavers, Larry 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 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 Stanislawski, Barry Kronenfeld, Barbara Buttenfield, Ethan 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 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 Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan Shavers, Alexander Duffy, Philip Thiem, Zhe Jiang, Adam Camerer
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 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 Kronenfeld, Larry Stanislawski, Barbara Buttenfield, Ethan Shavers
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 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 Shavers, Larry 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 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 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 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 Stanislawski, Ethan Shavers, Alexander Duffy, Philip Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry Kronenfeld, Barbara 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 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. Kirby, Arpan Sainju, Shaowen Wang, Larry Stanislawski, Ethan 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 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 Shavers, Larry Stanislawski, Philip Thiem, Dalia 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 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 Stanislawski, Ethan 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

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 Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Non-USGS Publications**

Anderson-Tarver CA, Gleason M, Buttenfield B, Stanislawski L (2012) Automated Centerline Delineation to Enrich the National Hydrography Dataset, In Xiao N et al. (eds), GIScience 2012, Lecture Notes in Computer Science 7478:15-28, Springer-Verlag Berlin Heidelberg
Anderson-Tarver CA, Buttenfield BP, Stanislawski LV, Koontz JM (2011) Automated Delineation of Stream Centerlines for the USGS National Hydrography Dataset, In Ruas (ed), Advances in Cartography and GIScience Volume 1 (Lecture Notes in Geoinformation and Cartography):409-423, Springer-Verlag Berlin Heidelberg
Buttenfield BP, Stanislawski LV, and Brewer CA (2011) Adapting Generalization Tools to Physiographic Diversity for the United States National Hydrography Dataset. Cartography and Geographic Information Science 38(3):289-301
Stanislawski LV, Buttenfield BP (2011) Hydrographic Generalization Tailored to Dry Mountainous Regions. Cartography and Geographic Information Systems 38(2):117-125
Stanislawski LV (2009) Feature Pruning by Upstream Drainage Area to Support Automated Generalization of the United States National Hydrography Dataset. Computers, Environment and Urban Systems, 33: 325-333
Stanislawski LV, Dewitt BA, Shrestha R (1996) Estimating Positional Accuracy of Data Layers Within a GIS Through Error Propagation. Photogrammetric Engineering and Remote Sensing 62(4):429-433

**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.

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
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) provides high accuracy, high-resolution (HR) elevation data for the United States.

By
Core Science Systems Mission Area, National Geospatial Program, 3D Hydrography Program
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) provides high accuracy, high-resolution (HR) elevation data for the United States.

By
Core Science Systems Mission Area, National Geospatial Program, 3D Hydrography Program

Science and Products

Filter Total Items: 37

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, Larry Stanislawski, Ethan Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
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 Shavers, Larry 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 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 Stanislawski, Barry Kronenfeld, Barbara Buttenfield, Ethan 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 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 Stanislawski, Nattapon Jaroenchai, Shaowen Wang, Ethan Shavers, Alexander Duffy, Philip Thiem, Zhe Jiang, Adam Camerer
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 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 Kronenfeld, Larry Stanislawski, Barbara Buttenfield, Ethan Shavers
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 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 Shavers, Larry 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 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 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 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 Stanislawski, Ethan Shavers, Alexander Duffy, Philip Thiem, Nattapon Jaroenchai, Shaowen Wang, Zhe Jiang, Barry Kronenfeld, Barbara 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 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. Kirby, Arpan Sainju, Shaowen Wang, Larry Stanislawski, Ethan 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 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 Shavers, Larry Stanislawski, Philip Thiem, Dalia 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 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 Stanislawski, Ethan 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

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 Shavers
By
Core Science Systems Mission Area, Center of Excellence for Geospatial Information Science (CEGIS), National Geospatial Technical Operations Center

Non-USGS Publications**

Anderson-Tarver CA, Gleason M, Buttenfield B, Stanislawski L (2012) Automated Centerline Delineation to Enrich the National Hydrography Dataset, In Xiao N et al. (eds), GIScience 2012, Lecture Notes in Computer Science 7478:15-28, Springer-Verlag Berlin Heidelberg
Anderson-Tarver CA, Buttenfield BP, Stanislawski LV, Koontz JM (2011) Automated Delineation of Stream Centerlines for the USGS National Hydrography Dataset, In Ruas (ed), Advances in Cartography and GIScience Volume 1 (Lecture Notes in Geoinformation and Cartography):409-423, Springer-Verlag Berlin Heidelberg
Buttenfield BP, Stanislawski LV, and Brewer CA (2011) Adapting Generalization Tools to Physiographic Diversity for the United States National Hydrography Dataset. Cartography and Geographic Information Science 38(3):289-301
Stanislawski LV, Buttenfield BP (2011) Hydrographic Generalization Tailored to Dry Mountainous Regions. Cartography and Geographic Information Systems 38(2):117-125
Stanislawski LV (2009) Feature Pruning by Upstream Drainage Area to Support Automated Generalization of the United States National Hydrography Dataset. Computers, Environment and Urban Systems, 33: 325-333
Stanislawski LV, Dewitt BA, Shrestha R (1996) Estimating Positional Accuracy of Data Layers Within a GIS Through Error Propagation. Photogrammetric Engineering and Remote Sensing 62(4):429-433

**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.

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
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) provides high accuracy, high-resolution (HR) elevation data for the United States.

By
Core Science Systems Mission Area, National Geospatial Program, 3D Hydrography Program
Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction Thumbnail

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

Transferring Deep Learning Knowledge for Scaling Up Hydrographic Feature Extraction

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) provides high accuracy, high-resolution (HR) elevation data for the United States.

By
Core Science Systems Mission Area, National Geospatial Program, 3D Hydrography Program
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