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
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry 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...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
Generalisation operators 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...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Pia Bereuter, Sandro Savino, Cynthia A. Brewer
Adapting generalization tools to physiographic diversity for the united states national hydrography dataset Adapting generalization tools to physiographic diversity for the united states national hydrography dataset
This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate...
Authors
B.P. Buttenfield, L.V. Stanislawski, C.A. Brewer
Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset
The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived...
Authors
L.V. 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
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry 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...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
Generalisation operators 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...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Pia Bereuter, Sandro Savino, Cynthia A. Brewer
Adapting generalization tools to physiographic diversity for the united states national hydrography dataset Adapting generalization tools to physiographic diversity for the united states national hydrography dataset
This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate...
Authors
B.P. Buttenfield, L.V. Stanislawski, C.A. Brewer
Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset
The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived...
Authors
L.V. 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.