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: 40
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 T. 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
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.
Science and Products
Filter Total Items: 40
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 T. 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
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.