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
An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions
This paper describes a workflow for automating the extraction of elevation-derived stream lines using open source tools with parallel computing support and testing the effectiveness of procedures in various terrain conditions within the conterminous United States. Drainage networks are extracted from the US Geological Survey 1/3 arc-second 3D Elevation Program elevation data having a nominal cell
Authors
Larry V. Stanislawski, Kornelijus Survila, Jeffrey Wendel, Yan Liu, Barbara P. Buttenfield
Partial polygon pruning of hydrographic features in automated generalization
This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservo
Authors
Alexander K. Stum, Barbara P. Buttenfield, Larry V. Stanislawski
High performance computing to support multiscale representation of hydrography for the conterminous United States
The National Hydrography Dataset (NHD) for the United States furnishes a comprehensive set of vector features representing the surface-waters in the country (U.S. Geological Survey 2000). The high-resolution (HR) layer of the NHD is largely comprised of hydrographic features originally derived from 1:24,000-scale (24K) U.S. Topographic maps. However, in recent years (2009 to present) densified hyd
Authors
Larry V. Stanislawski, Yan Liu, Barbara P. Buttenfield, Kornelijus Survila, Jeffrey Wendel, Abdurraouf Okok
Measuring distance “as the horse runs”: Cross-scale comparison of terrain-based metrics
Distance metrics play significant roles in spatial modeling tasks, such as flood inundation (Tucker and Hancock 2010), stream extraction (Stanislawski et al. 2015), power line routing (Kiessling et al. 2003) and analysis of surface pollutants such as nitrogen (Harms et al. 2009). Avalanche risk is based on slope, aspect, and curvature, all directly computed from distance metrics (Gutiérrez 2012).
Authors
Barbara P. Buttenfield, M Ghandehari, S Leyk, Larry V. Stanislawski, M E Brantley, Yi Qiang
An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States
Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of
Authors
Jochen Wendel, Barbara P. Buttenfield, Larry V. Stanislawski
Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing
Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural drainage densi
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
A rapid approach for automated comparison of independently derived stream networks
This paper presents an improved coefficient of line correspondence (CLC) metric for automatically assessing the similarity of two different sets of linear features. Elevation-derived channels at 1:24,000 scale (24K) are generated from a weighted flow-accumulation model and compared to 24K National Hydrography Dataset (NHD) flowlines. The CLC process conflates two vector datasets through a raster l
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Ariel T. Doumbouya
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry
Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
Generalisation operators
This chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Pia Bereuter, Sandro Savino, Cynthia A. Brewer
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: 33
An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions
This paper describes a workflow for automating the extraction of elevation-derived stream lines using open source tools with parallel computing support and testing the effectiveness of procedures in various terrain conditions within the conterminous United States. Drainage networks are extracted from the US Geological Survey 1/3 arc-second 3D Elevation Program elevation data having a nominal cell
Authors
Larry V. Stanislawski, Kornelijus Survila, Jeffrey Wendel, Yan Liu, Barbara P. Buttenfield
Partial polygon pruning of hydrographic features in automated generalization
This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservo
Authors
Alexander K. Stum, Barbara P. Buttenfield, Larry V. Stanislawski
High performance computing to support multiscale representation of hydrography for the conterminous United States
The National Hydrography Dataset (NHD) for the United States furnishes a comprehensive set of vector features representing the surface-waters in the country (U.S. Geological Survey 2000). The high-resolution (HR) layer of the NHD is largely comprised of hydrographic features originally derived from 1:24,000-scale (24K) U.S. Topographic maps. However, in recent years (2009 to present) densified hyd
Authors
Larry V. Stanislawski, Yan Liu, Barbara P. Buttenfield, Kornelijus Survila, Jeffrey Wendel, Abdurraouf Okok
Measuring distance “as the horse runs”: Cross-scale comparison of terrain-based metrics
Distance metrics play significant roles in spatial modeling tasks, such as flood inundation (Tucker and Hancock 2010), stream extraction (Stanislawski et al. 2015), power line routing (Kiessling et al. 2003) and analysis of surface pollutants such as nitrogen (Harms et al. 2009). Avalanche risk is based on slope, aspect, and curvature, all directly computed from distance metrics (Gutiérrez 2012).
Authors
Barbara P. Buttenfield, M Ghandehari, S Leyk, Larry V. Stanislawski, M E Brantley, Yi Qiang
An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States
Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of
Authors
Jochen Wendel, Barbara P. Buttenfield, Larry V. Stanislawski
Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing
Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural drainage densi
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
A rapid approach for automated comparison of independently derived stream networks
This paper presents an improved coefficient of line correspondence (CLC) metric for automatically assessing the similarity of two different sets of linear features. Elevation-derived channels at 1:24,000 scale (24K) are generated from a weighted flow-accumulation model and compared to 24K National Hydrography Dataset (NHD) flowlines. The CLC process conflates two vector datasets through a raster l
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Ariel T. Doumbouya
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry
Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
Generalisation operators
This chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Pia Bereuter, Sandro Savino, Cynthia A. Brewer
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.