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Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 1 (March 2020) . - pp 4 - 21 Titre : Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks Type de document : Article/Communication Auteurs : Lauwrence V. Stanislawski, Auteur ; Michael P. Finn, Auteur ; Barbara P. Buttenfield, Auteur Année de publication : 2020 Article en page(s) : pp 4 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] altitude [Termes IGN] base de données hydrographiques [Termes IGN] cartographie des flux [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] cours d'eau [Termes IGN] drainage [Termes IGN] Etats-Unis [Termes IGN] pente [Termes IGN] perméabilité du sol [Termes IGN] représentation multiple [Termes IGN] réseau hydrographique [Termes IGN] ruissellement [Termes IGN] segmentation [Termes IGN] traitement automatique de données [Vedettes matières IGN] Généralisation Résumé : (auteur) 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. Numéro de notice : A2020-070 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2018.1443759 Date de publication en ligne : 20/03/2018 En ligne : https://doi.org/10.1080/23729333.2018.1443759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94632 [article]

Partial polygon pruning of hydrographic features in automated generalization / Alexander K. Stum in Transactions in GIS, vol 21 n° 5 (October 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 5 (October 2017) . - pp 1061–1078 Titre : Partial polygon pruning of hydrographic features in automated generalization Type de document : Article/Communication Auteurs : Alexander K. Stum, Auteur ; Barbara P. Buttenfield, Auteur ; Lauwrence V. Stanislawski, Auteur Année de publication : 2017 Article en page(s) : pp 1061–1078 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données hydrographiques [Termes IGN] détection automatique [Termes IGN] Etats-Unis [Termes IGN] généralisation automatique de données [Termes IGN] petite échelle [Termes IGN] polygone [Termes IGN] rendu (géovisualisation) [Termes IGN] simplification de contour [Termes IGN] traitement automatique de données [Vedettes matières IGN] Généralisation Résumé : (Auteur) 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 features – such 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. Numéro de notice : A2017-634 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12270 En ligne : http://dx.doi.org/10.1111/tgis.12270 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86953 [article]

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States / Jochen Wendel in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 233 - 249 Titre : An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States Type de document : Article/Communication Auteurs : Jochen Wendel, Auteur ; Barbara P. Buttenfield, Auteur ; Lauwrence V. Stanislawski, Auteur Année de publication : 2016 Article en page(s) : pp 233 - 249 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] données hydrographiques [Termes IGN] Etats-Unis [Termes IGN] généralisation cartographique automatisée [Termes IGN] intégration de données [Termes IGN] système d'information géographique Résumé : (Auteur) 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 outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification. Numéro de notice : A2016-166 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1067829 En ligne : https://doi.org/10.1080/15230406.2015.1067829 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80472 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A rapid approach for automated comparison of independently derived stream networks / Lauwrence V. Stanislawski in Cartography and Geographic Information Science, Vol 42 n° 5 (November 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 5 (November 2015) . - pp 435 - 448 Titre : A rapid approach for automated comparison of independently derived stream networks Type de document : Article/Communication Auteurs : Lauwrence V. Stanislawski, Auteur ; Barbara P. Buttenfield, Auteur ; Ariel Doumbouya, Auteur Année de publication : 2015 Article en page(s) : pp 435 - 448 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse comparative [Termes IGN] base de données hydrographiques [Termes IGN] bassin hydrographique [Termes IGN] cartographie des flux [Termes IGN] modélisation spatiale [Termes IGN] réseau hydrographique Résumé : (Auteur) 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. Numéro de notice : A2015-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1060869 En ligne : https://doi.org/10.1080/15230406.2015.1060869 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77602 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2015051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Abstracting geographic information in a data rich world, ch. 11. Generalisation in practice within national mapping agencies / Cécile Duchêne (2014)  

Public contenu dans Abstracting geographic information in a data rich world / Dirk Burghardt (2014) Titre de série : Abstracting geographic information in a data rich world, ch. 11 Titre : Generalisation in practice within national mapping agencies Type de document : Chapitre/Contribution Auteurs : Cécile Duchêne , Auteur ; Blanca Baella, Auteur ; Cynthia A. Brewer, Auteur ; Dirk Burghardt, Auteur ; Barbara P. Buttenfield, Auteur ; Julien Gaffuri , Auteur ; Dominik Käuferle, Auteur ; François Lecordix , Auteur ; Emmanuel Maugeais , Auteur ; Ron Nijhuis, Auteur ; Maria Pla, Auteur ; Marc Post, Auteur ; Nicolas Regnauld , Auteur ; Lauwrence V. Stanislawski, Auteur ; Jantien E. Stoter, Auteur ; Katalin T'oth, Auteur ; Sabine Urbanke, Auteur ; Vincent Van Altena, Auteur ; Antje Wiedemann, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2014 Collection : Lecture notes in Geoinformation and Cartography, ISSN 1863-2246 Importance : pp 329 - 391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données localisées [Termes IGN] chaîne de production [Termes IGN] chaîne de traitement [Termes IGN] généralisation automatique de données [Termes IGN] généralisation cartographique automatisée [Termes IGN] représentation multiple [Vedettes matières IGN] Généralisation Résumé : (auteur) National Mapping Agencies (NMAs) are still among the main end users of research into automated generalisation, which is transferred into their production 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 being obtained and what further developments are on-going or planned. A contribution by the European Joint Research Center reports on the use of multiple representation and generalisation in the context of the implementation of the European INSPIRE directive. The chapter finishes with a synthesis of recent achievements, as well as future challenges that NMAs have begun to tackle. Numéro de notice : H2014-008 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1007/978-3-319-00203-3_11 Date de publication en ligne : 01/04/2014 En ligne : http://dx.doi.org/10.1007/978-3-319-00203-3_11 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78635

Automated thinning of road networks and road labels for multiscale design of The National Map of the United States / Cynthia A. Brewer in Cartography and Geographic Information Science, vol 40 n° 4 (September 2013)  

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