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Is map generalisation a computational cartography problem? / Guillaume Touya (2022)  

Public Titre : Is map generalisation a computational cartography problem? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2022 Projets : LostInZoom / Touya, Guillaume Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] généralisation cartographique automatisée [Termes IGN] géomètrie algorithmique [Vedettes matières IGN] Généralisation Résumé : (auteur) Map generalisation is one of the processes of map design, when the spatial data used to make the map is too detailed for the scale of the map. Map generalisation seeks to abstract and simplify this detailed spatial information to represent it on the map with a good legibility. Map generalisation involves different types of transformations: selection, simplification, displacement, aggregation, collapse, etc. Historically, computational geometry researchers greatly contributed to the automation of map generalisation by proposing algorithms to achieve some of these atomic transformations. But the research questions addressed by researchers on map generalisation have changed over the years, with the progress on the automation, and the changes in the way people use maps (apparition of multi-scale zoomable web maps). Is map generalisation still a computational cartography problem? To discuss this rhetorical question, this presentation illustrates the current issues of map generalisation, with past contribution of computational cartography and possible computational cartography problems. For instance, there are still needs for new specific algorithms, either because maps are much more diverse, or because the larger scale range calls for new transformations. As maps tend to mix elements of topographic maps and elements of schematized maps, computational cartography is also relevant to solve this task. Finally, as the current trend in map generalisation is the use of deep learning models, the presentation discusses the relevance of computational cartography techniques in this context. Numéro de notice : C2022-028 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Conférence invitée nature-HAL : Conf-Invitée DOI : sans En ligne : https://hal.science/hal-03677309v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100959

Schematizing crossroads from abstract textual descriptions / Jean-Marie Favreau (2022)  

Public Titre : Schematizing crossroads from abstract textual descriptions Type de document : Article/Communication Auteurs : Jean-Marie Favreau, Auteur ; Guillaume Touya , Auteur ; Jérémy Kalsron, Auteur Editeur : Bonn : Université de Bonn Année de publication : 2022 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Importance : 3 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carrefour [Termes IGN] carte tactile [Termes IGN] cartogramme [Termes IGN] exploration de texte [Vedettes matières IGN] Cartologie Résumé : (auteur) [début] The use of cartographic representations among people with visual impairments (PVI) is often limited by the lack of available materials. However, two uses have been identified: diagrams made with sticks magnetised to a metal plate (Figure 1) are used by Orientation and Mobility instructors as a discussion aid around complex areas (typically intersections), and more accurate maps made by transcribing adapters are sometimes produced for regular use. While classical variations of the generalisation and stylisation approaches allow for the production of fairly accurate maps [JLCJ21], for example from OpenStreetMap data (figure 2), there are currently no known approaches to producing a more schematic representation, in the manner of the locomotion instructors’ magnets. Numéro de notice : C2022-013 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Date de publication en ligne : 24/05/2022 En ligne : https://hal.science/hal-03677334/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100747