Détail de l'autorité
ICA 2017, 20th ICA Workshop on Generalisation and Multiple Representation 01/07/2017 02/07/2017 Washington DC Etats-Unis OA program
nom du congrès :
ICA 2017, 20th ICA Workshop on Generalisation and Multiple Representation
début du congrès :
01/07/2017
fin du congrès :
02/07/2017
ville du congrès :
Washington DC
pays du congrès :
Etats-Unis
site des actes du congrès :
|
Documents disponibles (3)



Titre : Mapping heterogeneous data: a case study on the French Green Infrastructure Type de document : Article/Communication Auteurs : Cécile Duchêne , Auteur ; Sébastien Mustière
, Auteur ; Sandrine Gomes, Auteur ; Mathilde Kremp, Auteur ; Lucille Billon, Auteur ; Romain Sordello, Auteur
Editeur : ICA Commission on Generalisation and Multiple Representation Année de publication : 2017 Conférence : ICA 2017, 20th ICA Workshop on Generalisation and Multiple Representation 01/07/2017 02/07/2017 Washington DC Etats-Unis OA program Importance : 9 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] carte thématique
[Termes IGN] données hétérogènes
[Termes IGN] France (administrative)
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] harmonisation des données
[Termes IGN] interopérabilité
[Termes IGN] trame verte et bleue
[Termes IGN] utilisation du solRésumé : (auteur) To achieve a preservation and restoration of ecosystems, public environmental policies at the international level are fostering the implementation of Green Infrastructures, i.e. networks composed of areas where animal and vegetal species can live (habitat patches), and corridors to circulate between them. In France, defining existing habitat patches and corridors was ensured in a distributed manner by the Regions, the first subnational administrative level, with flexible guidelines. It resulted in very heterogeneous data in terms of level of detail, raising the question: “How to map such heterogeneous data at a supra-regional level, making them understandable while respecting the work of Regions, and with a reasonable amount of human work?”. Our study focuses on habitat patches of two adjacent Regions. After making a “rough” map directly from the provided data, we explore three ways for homogenizing the map. The first method consists in generalizing the more detailed data using simple morphologic operators. The second method consists in graphically refining the less detailed data by filling the areas with a pattern taken from the more detailed data. The third method consists in drastically changing the level of abstraction of the data on both regions, while rasterizing the space. Although it would be necessary to test the resulting maps on potential users, we think the third approach is probably the only one usable. Numéro de notice : C2017-035 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89950 Documents numériques
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Mapping heterogeneous data ... - pdf éditeurAdobe Acrobat PDFProceedings of 20th ICA Workshop on Generalisation and Multiple Representation / International cartographic association = association cartographique internationale (2017)
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Titre : Proceedings of 20th ICA Workshop on Generalisation and Multiple Representation Type de document : Actes de congrès Auteurs : International cartographic association = association cartographique internationale, Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2017 Conférence : ICC 2017, 28th International Cartographic Conference ICA 02/07/2017 07/07/2017 Washington DC Etats-Unis OA Proceedings of the ICA, ICA 2017, 20th ICA Workshop on Generalisation and Multiple Representation 01/07/2017 02/07/2017 Washington DC Etats-Unis OA program Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Généralisation Numéro de notice : 22748 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Actes DOI : sans En ligne : http://generalisation.icaci.org/prevevents.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86196 Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas / Guillaume Touya (2017)
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Titre : Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Marion Dumont
, Auteur
Editeur : ICA Commission on Generalisation and Multiple Representation Année de publication : 2017 Conférence : ICA 2017, 20th ICA Workshop on Generalisation and Multiple Representation 01/07/2017 02/07/2017 Washington DC Etats-Unis OA program Importance : 10 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse multicritère
[Termes IGN] apprentissage automatique
[Termes IGN] base de données cartographiques
[Termes IGN] estompage automatique
[Termes IGN] généralisation du bâti
[Termes IGN] point de repère
[Termes IGN] relation spatiale
[Termes IGN] zone urbaine
[Termes IGN] zoom
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Geovisualization applications that allow the navigation between maps at different scales while zooming in and out often provide no smooth transition between the individual building level of abstraction and the representation of whole urban areas as polygons. In order to reduce the cognitive load of the user, we seek to add intermediate zoom levels with intermediate and progressive abstractions between buildings and urban areas. This paper proposes a method based on progressive block graying while enhancing building landmarks, to derive these intermediate representations from the individual buildings. Block graying is based on an automatic building classification, and a multiple criteria decision technique to infer inner city blocks. The landmarks identification relies on machine learning and several criteria based on geometry and spatial relations. The method is tested with real cartographic data between the 1:25k (with individual buildings) and the 1:100k scale (with urban areas): transitions with one, two, or three intermediate representations are derived and tested. Numéro de notice : C2017-007 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86201 Documents numériques
en open access
Progressive_Block_Graying_and_Landmarks_Enhancing - postprintAdobe Acrobat PDF