Papers by Lawrence V. Stanislawski
This dataset comprises original and simplified versions of hydrographic 28 flowline features in N... more This dataset comprises original and simplified versions of hydrographic 28 flowline features in North Dakota, USA. The original data was derived from National Hydrography Dataset (NHD) data for the 1:24,000 Sheep Creek Dam topographic map quadrangle. Flowline features were selected for 1:100,000 scale (100k) representation using the NHD VisibilityFilter attribute and further filtered and merged to form a contiguous network. Features were then simplified for 100k representation using a combination of automated and manual operations. The dataset was created for the 24<sup>th</sup> ICA Workshop on Map Generalisation and Multiple Representation; further details can be found in the workshop abstract. The dataset is intended to serve as a benchmark for cartographic generalization algorithms used to simplify and smooth hydrographic flowline features. <strong>Statistics:</strong> Original vertices: 9620 Simplified vertices: 1485 Vertex reduction: 84.6% <strong>...
Recent (2007) developments in automated integration of vector geospatial data with image data com... more Recent (2007) developments in automated integration of vector geospatial data with image data combine techniques for extracting point features from image data with algorithms that match point features between data layers. The precision and accuracy of an image-extracted point feature depend on various factors related to the feature extraction technique and to the quality of the image in the area of extraction. Furthermore, the precision and accuracy of image-extracted points affect the reliability of the subsequent process for matching points between layers, and consequently, the overall adequacy of the data integration approach. Several approaches for detecting and removing improperly matched points are available. The USGS is investigating the use of a weighted affine transformation to filter point matches during automated integration of vector roads with images. The transformation is applied to a local area of match points to detect probable blunders and remove them from the rubber-sheeting algorithm. Aside from blunder detection capabilities, advantages of this approach include the ability to weight control coordinates relative to estimated precisions of extracted point features, and the ability to estimate the precision of the integrated vector layer through error propagation.
ACM Transactions on Intelligent Systems and Technology, Jan 5, 2022
Zhe Jiang, et al. reserve geometric properties (e.g., spatial contiguity within line segments). E... more Zhe Jiang, et al. reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy. CCS Concepts: • Information systems → Geographic information systems; Data mining; • Applied computing → Earth and atmospheric sciences; • Computing methodologies → Artificial intelligence.
Computers, Environment and Urban Systems, Sep 1, 2009
The United States Geological Survey has been researching generalization approaches to enable mult... more 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 from knowledge of surface water, the NHD model, and associated feature specification standards. Relative prominence of network features is estimated from upstream drainage area (UDA). Network and polygon features are pruned by UDA and NHD reach code to achieve a drainage density appropriate for any less detailed map scale. Data partitioning maintains local drainage density variations that characterize the terrain. For demonstration, a 48-subbasin area of 1:24 000-scale NHD was pruned to 1:100 000-scale (100K) and compared to a benchmark, the 100K NHD. The coefficient of line correspondence (CLC) is used to evaluate how well pruned network features match the benchmark network. CLC values of 0.82 and 0.77 result from pruning with and without partitioning, respectively. The number of polygons that remain after pruning is about seven times that of the benchmark, but the area covered by the polygons that remain after pruning is only about 10 percent greater than the area covered by benchmark polygons.
Geomorphology, Sep 1, 2023
International journal of cartography, Mar 20, 2018
Automated generalization software must accommodate multi-scale representations of hydrographic ne... more 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 characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions.
Cartography and Geographic Information Science, Jul 4, 2017
ABSTRACT This paper describes a workflow for automating the extraction of elevation-derived strea... more ABSTRACT 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 size of 10 m. This research demonstrates the utility of open source tools with parallel computing support for extracting connected drainage network patterns and handling depressions in 30 subbasins distributed across humid, dry, and transitional climate regions and in terrain conditions exhibiting a range of slopes. Special attention is given to low-slope terrain, where network connectivity is preserved by generating synthetic stream channels through lake and waterbody polygons. Conflation analysis compares the extracted streams with a 1:24,000-scale National Hydrography Dataset flowline network and shows that similarities are greatest for second- and higher-order tributaries.
Environmental Modelling and Software, Oct 1, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Cartography and Geographic Information Science, Sep 15, 2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Sep 19, 2018
High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 met... more 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 and overpasses, can form barriers that incorrectly alter flow accumulation models, and hinder the extraction of accurate surface water drainage networks. This study tests a deep learning approach to identify the intersections of roads and stream valleys, whereby valley channels can be burned through road embankments in a HR DEM for subsequent flow accumulation modeling, and proper natural drainage network extraction.
Transactions in Gis, Mar 8, 2017
This article demonstrates a working method to automatically detect and prune portions of waterbod... more This article 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 sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal featuressuch as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US.
Cartography and Geographic Information Science, Sep 1, 2013
ABSTRACT This paper reports on progress in generalization and selective feature removal for a sub... more ABSTRACT This paper reports on progress in generalization and selective feature removal for a subset of fundamental base map layers that enables competent mapping through scales ranging from 1:24,000 to 1:1,000,000. Thinning and partitioning methods are applied to road features and labels for The National Map of the United States. Roads are thinned adaptively using the ArcGIS Thin Road Network geoprocessing tool, which removes features by feature hierarchy and network connectivity, yet preserves characteristic urban/rural local density patterns that can be lost through simple category removals. The paper describes thinning for label hierarchies within road categories, improved preference in placement for more important road labels, and selective removal of labels through scale. Use of the Radical Law to guide matches between thinning parameters and suitable scales of representation also is shown. Inspection of graphic results of these treatments can help to establish parameters for automated base map design for US topographic mapping.
Environmental Modelling and Software, Jun 1, 2021
Abstract Surface water is an irreplaceable resource for human survival and environmental sustaina... more Abstract 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 flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.
Cartography and Geographic Information Science, 2011
ABSTRACT This paper reports on generalization and data modeling to create reduced scale versions ... more ABSTRACT 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 (dry or humid) can support decisions about algorithms, parameters, and processing sequences to create generalized, smaller scale data versions which preserve distinct hydrographic patterns in these regions. We work with multiple subbasins of the NHD that provide a range of terrain and climate characteristics. Specifically tailored generalization sequences are used to create simplified versions of the high resolution data, which was compiled for 1:24,000 scale mapping. Results are evaluated cartographically and metrically against a medium resolution benchmark version compiled for 1:100,000, developing coefficients of linear and areal correspondence.
Cartography and Geographic Information Science, 2011
A wide variety of climate and terrain conditions exist in the United States and optimal cartograp... more A wide variety of climate and terrain conditions exist in the United States and optimal cartographic generalization techniques for one area of the country may not be suitable for another, particularly when working with surface hydrographic data. This paper presents generalization and data modelling to produce reduced scale versions of hydrographic data for a multi-resolution national data set, The National Map, of the United States Geological Survey (USGS). The approach distinguishes regional differences in geographic factors to demonstrate that knowledge about varying terrain and climate conditions can support the design of tailored generalization operations that preserve distinct hydrographic patterns. Hydrographic generalization procedures are being tailored for different terrain (mountainous, hilly, and flat) and climate (humid and dry) conditions within the United States. We demonstrate using a sequence of automated generalization operations tailored for a dry mountainous subbasin watershed of the United States National Hydrography Dataset (NHD). NHD data for the subbasin, compiled from 1:24,000-scale source material, were generalized to create hydrographic data that are appropriate for cartographic mapping at scales between about 1:50,000 and 1:200,000. Generalization results are metrically compared to a 1:100,000-scale NHD benchmark through the Coefficient of Line Correspondence (CLC) and the Coefficient of Area Correspondence (CAC). Confidence intervals for the CLC and CAC are generated through a non-parametric bootstrapping approach. These metrics and associated confidence intervals can help establish the geographic extents that are suitable for each set of tailored generalization procedures.
Cartography and Geographic Information Science, Aug 14, 2015
This paper presents an improved coefficient of line correspondence (CLC) metric for automatically... more 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 line-density differencing approach that is faster and more reliable than earlier methods. Methods are tested on 30 subbasins distributed across different terrain and climate conditions of the conterminous United States. CLC values for the 30 subbasins indicate 44–83% of the features match between the two datasets, with the majority of the mismatching features comprised of first-order features. Relatively lower CLC values result from subbasins with less than about 1.5 degrees of slope. The primary difference between the two datasets may be explained by different data capture criteria. First-order, headwater tributaries derived from the flow-accumulation model are captured more comprehensively through drainage area and terrain conditions, whereas capture of headwater features in the NHD is cartographically constrained by tributary length. The addition of missing headwaters to the NHD, as guided by the elevation-derived channels, can substantially improve the scientific value of the NHD.
An investigation was conducted to develop guidelines for performing and analyzing network adjustm... more An investigation was conducted to develop guidelines for performing and analyzing network adjustments of baseline measurements collected with Global Positioning System relative positioning techniques. Four second order class I geodetic control networks were established along transportation corridors according to the standards and specifications described by the Federal Geodetic Control Committee. Four Trimble 4000SX receivers were used to collect the data. Each of the four networks was adjusted independently on two different datums, NAD27 and NAD83. The results of the NAD83 adjustments showed station error ellipses that have semi-major axes ranging between 0.008 m and 0.090 m at the 95% level of confidence, and baseline precisions that range between 0.126 PPM and 13.096 PPM. The NAD27 adjustments showed error ellipses having semi-major axes that range between 0.023 m and 0.935 m at the 95% level of confidence and baseline precisions ranging between 1.315 PPM and 26.172 PPM. Some guidelines and recommendations for performing weighted least squares adjustments of GPS networks are provided.
A wide variety of climate and terrain conditions exist in the United States and optimal cartograp... more A wide variety of climate and terrain conditions exist in the United States and optimal cartographic generalization techniques for one area of the country may not be suitable for another, particularly when working with surface hydrographic data. This paper presents generalization and data modelling to produce reduced scale versions of hydrographic data for a multi-resolution national data set, The National Map, of the United States Geological Survey (USGS). The approach distinguishes regional differences in geographic factors to demonstrate that knowledge about varying terrain and climate conditions can support the design of tailored generalization operations that preserve distinct hydrographic patterns. Hydrographic generalization procedures are being tailored for different terrain (mountainous, hilly, and flat) and climate (humid and dry) conditions within the United States. We demonstrate using a sequence of automated generalization operations tailored for a dry mountainous subbasin watershed of the United States National Hydrography Dataset (NHD). NHD data for the subbasin, compiled from 1:24,000-scale source material, were generalized to create hydrographic data that are appropriate for cartographic mapping at scales between about 1:50,000 and 1:200,000. Generalization results are metrically compared to a 1:100,000-scale NHD benchmark through the Coefficient of Line Correspondence (CLC) and the Coefficient of Area Correspondence (CAC). Confidence intervals for the CLC and CAC are generated through a non-parametric bootstrapping approach. These metrics and associated confidence intervals can help establish the geographic extents that are suitable for each set of tailored generalization procedures.
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Papers by Lawrence V. Stanislawski