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 and his family moved to Rolla, Missouri where 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: 33
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 derived from cha
Authors
Ethan J. Shavers, Larry Stanislawski
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 questions. What m
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
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 must manage r
Authors
Ting Li, Larry Stanislawski, Tyler Brockmeyer, Shaowen Wang, Ethan J. Shavers
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 plot, which
Authors
Larry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld, Ethan J. Shavers
Simplification of polylines by segment collapse: Minimizing areal displacement while preserving area
This paper reports on a new Area Preserving Segment Collapse (APSC) algorithm for simplifying polygonal boundaries while preserving polygonal area at simplified target scales and minimizing areal displacement. A general segment collapse algorithm is defined by iteratively collapsing segments to Steiner points in priority order, guided by placement and displacement functions. The algorithm is speci
Authors
Barry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Tyler Brockmeyer
Generalization in practice within national mapping agencies
National Mapping Agencies (NMAs) are still among the main end users of research into automated generalisation, which is transferred into their produc- tion lines via various means. This chapter includes contributions from seven NMAs, illustrating how automated generalisation is used in practice within their partly or fully automated databases and maps production lines, what results are currently b
Authors
Cécile Duchêne, Blanca Baella, Cynthia A. Brewer, Dirk Burghardt, Barbara P. Buttenfield, Julien Gaffuri, Dominik Käuferle, Francois Lecordix, Emmanuel Maugeais, Ron Nijhuis, Maria Pla, Marc Post, Nicolas Regnauld, Larry Stanislawski, Jantien Stoter, Katalin Tóth, Sabine Urbanke, Vincent van Altena, Antje Wiedemann
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 hydrologic modeli
Authors
Ethan J. Shavers, Larry V. Stanislawski
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 as roads a
Authors
Larry Stanislawski, Tyler Brockmeyer, Ethan J. Shavers
Area-preserving simplification of polygon features
Developing simplified representations of a two-dimensional polyline is an important problem in cartographic data analytics where datasets must be integrated across spatial resolutions. This problem is generally referred to as line simplification, and is increasingly driven by preservation of specific analytic properties such as positional accuracy and high-frequency detail. However, the distinctio
Authors
Barry J. Kronenfeld, Larry V. Stanislawski, Tyler Brockmeyer, Barbara P. Buttenfield
Similarity assessment of linear hydrographic features using high performance computing
This work discusses a current open source implementation of a line similarity assessment workflow to compare elevation-derived drainage lines with the high-resolution National Hydrography Dataset (NHD) surface-water flow network. The process identifies matching and mismatching lines in each dataset to help focus subsequent validation procedures to areas of the NHD that more critically need updates
Authors
Larry V. Stanislawski, Jeffrey Wendel, Ethan J. Shavers, Ting Li
Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks
Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and c
Authors
Larry V. Stanislawski, Michael P. Finn, Barbara P. Buttenfield
Generalizing linear stream features to preserve sinuosity for analysis and display: A pilot study in multi-scale data science
Cartographic generalization can impact geometric properties of geospatial data and subsequent analyses. This study evaluates simplification methods with the goal of preserving geometric details, such as sinuosity. We evaluate two recently developed line simplification algorithms that introduce Steiner points: Raposo’s Spatial Means, and Kronenfeld’s new area-preserving segment collapse algorithm,
Authors
Larry V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Tyler (Contractor) Brockmeyer
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.
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Filter Total Items: 33
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 derived from chaAuthorsEthan J. Shavers, Larry StanislawskiPreserving 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 questions. What mAuthorsEthan J. Shavers, Larry Stanislawski, Barbara P. Buttenfield, Barry J. KronenfeldOpenCLC: 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 must manage rAuthorsTing Li, Larry Stanislawski, Tyler Brockmeyer, Shaowen Wang, Ethan J. ShaversScale-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 plot, whichAuthorsLarry Stanislawski, Barbara P. Buttenfield, Barry J. Kronenfeld, Ethan J. ShaversSimplification of polylines by segment collapse: Minimizing areal displacement while preserving area
This paper reports on a new Area Preserving Segment Collapse (APSC) algorithm for simplifying polygonal boundaries while preserving polygonal area at simplified target scales and minimizing areal displacement. A general segment collapse algorithm is defined by iteratively collapsing segments to Steiner points in priority order, guided by placement and displacement functions. The algorithm is speciAuthorsBarry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Tyler BrockmeyerGeneralization in practice within national mapping agencies
National Mapping Agencies (NMAs) are still among the main end users of research into automated generalisation, which is transferred into their produc- tion lines via various means. This chapter includes contributions from seven NMAs, illustrating how automated generalisation is used in practice within their partly or fully automated databases and maps production lines, what results are currently bAuthorsCécile Duchêne, Blanca Baella, Cynthia A. Brewer, Dirk Burghardt, Barbara P. Buttenfield, Julien Gaffuri, Dominik Käuferle, Francois Lecordix, Emmanuel Maugeais, Ron Nijhuis, Maria Pla, Marc Post, Nicolas Regnauld, Larry Stanislawski, Jantien Stoter, Katalin Tóth, Sabine Urbanke, Vincent van Altena, Antje WiedemannStreams 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 hydrologic modeliAuthorsEthan J. Shavers, Larry V. StanislawskiAutomated 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 as roads aAuthorsLarry Stanislawski, Tyler Brockmeyer, Ethan J. ShaversArea-preserving simplification of polygon features
Developing simplified representations of a two-dimensional polyline is an important problem in cartographic data analytics where datasets must be integrated across spatial resolutions. This problem is generally referred to as line simplification, and is increasingly driven by preservation of specific analytic properties such as positional accuracy and high-frequency detail. However, the distinctioAuthorsBarry J. Kronenfeld, Larry V. Stanislawski, Tyler Brockmeyer, Barbara P. ButtenfieldSimilarity assessment of linear hydrographic features using high performance computing
This work discusses a current open source implementation of a line similarity assessment workflow to compare elevation-derived drainage lines with the high-resolution National Hydrography Dataset (NHD) surface-water flow network. The process identifies matching and mismatching lines in each dataset to help focus subsequent validation procedures to areas of the NHD that more critically need updatesAuthorsLarry V. Stanislawski, Jeffrey Wendel, Ethan J. Shavers, Ting LiClassifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks
Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and cAuthorsLarry V. Stanislawski, Michael P. Finn, Barbara P. ButtenfieldGeneralizing linear stream features to preserve sinuosity for analysis and display: A pilot study in multi-scale data science
Cartographic generalization can impact geometric properties of geospatial data and subsequent analyses. This study evaluates simplification methods with the goal of preserving geometric details, such as sinuosity. We evaluate two recently developed line simplification algorithms that introduce Steiner points: Raposo’s Spatial Means, and Kronenfeld’s new area-preserving segment collapse algorithm,AuthorsLarry V. Stanislawski, Barry J. Kronenfeld, Barbara P. Buttenfield, Tyler (Contractor) BrockmeyerNon-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 HeidelbergAnderson-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 HeidelbergButtenfield 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):289-301Stanislawski LV, Buttenfield BP (2011) Hydrographic Generalization Tailored to Dry Mountainous Regions. Cartography and Geographic Information Systems 38(2):117-125Stanislawski 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: 325-333Stanislawski 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.