Détail de l'autorité
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ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie
Autorités liées :
nom du congrès :
ICC 2021, 30th ICA international cartographic conference
date de début du congrès :
14/12/2021
date de fin du congrès :
18/12/2021
ville du congrès :
Florence
pays du congrès :
Italie
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Documents disponibles (15)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesAn attempt to define perceptive and sensitive mapping through lived space experiments / Catherine Dominguès (2021)
Titre : An attempt to define perceptive and sensitive mapping through lived space experiments Type de document : Article/Communication Auteurs : Catherine Dominguès Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA num. 3 Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des besoins
[Termes IGN] cartographie sensible
[Termes IGN] expérience scientifique
[Termes IGN] utilisateur
[Vedettes matières IGN] CartologieRésumé : (auteur) [début] Maps are often used in the context of human and social sciences, including as a tool. For example, maps as graphic tools enable to locate survey fields and data. Especially, the synoptic property of maps makes it possible to investigate the spatial dimension of a phenomenon, the distribution of data, its changes over time, etc. In teaching activities and in support tasks for research at the EHESS in Paris, difficulties have arisen in showing research data and results in a manner which would be fruitful and acceptable to the students and researchers. The need for an adapted mapping has emerged, including the map-making process and the achieved map. Adapted mapping has been named by the phrase perceptive and sensitive mapping, in contrast with conventional mapping based on geographical databases, GIS tools and the theory of graphic semiology as taught by Jacques Bertin (Bertin, 1983). In response to this need, a training methodological seminar has been set up since 2016 in EHESS. It aims at providing an (organizational and material) framework for students in which they can experiment various protocols and be confronted with different data specifications. The procedures are designed in order to accentuate specific aspects that are not supposed to be fulfilled by conventional mapping. An analysis has been performed targeting the students' achievements and how they have been achieved. The analysis makes it possible to characterize the maps drawn in this context; to compare the students' difficulties and comments with the needs they initially expressed; to highlight in which cases conventional cartography may be inadequate for laying out some data. The result analysis enabled considering three questions: how may conventional mapping and perceptive and sensitive mapping be compared? How is perceptive and sensitive mapping a relevant tool? And thanks to the answers of the previous questions: What would be a definition of perceptive and sensitive mapping? To this end, the paper firstly details how the needs for maps were expressed and how the seminar tried to answer them by defining experiments. In the second section, the achievements are analyzed based on two items: the (displayed) graphical and cartographic features, and the protocols which enabled to make them. Lastly, the analysis enables to offer a definition of perceptive and sensitive mapping by means of a comparison with conventional mapping. Numéro de notice : C2021-044 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-70-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-70-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99394
Titre : Can graph convolution networks learn spatial relations? Type de document : Article/Communication Auteurs : Azelle Courtial Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA num. 3 Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] alignement
[Termes IGN] bati
[Termes IGN] objet géographique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [introduction] Maps are composed of spatially related geographic objects. Spatial relations are key information for human as they support the description of relative locations: the house is to the east of the city centre, near the interchange, or at the end of the path. Consequently, preserving these spatial relations is important during map generalisation. For example, building typification is a generalisation operation that seeks to reduce the quantity of building while preserving relation between and within homogeneous buildings groups (Regnauld, 2001). Building or road patterns are remarkable distributions of elements in the map from which high-level concepts and semantics (e.g. landuse types and urban morphology) can be inferred. Such patterns can be characterized by spatial relations (e.g. proximity, similarity and continuity of these elements) and hence are visually easy to identify by a human. To identify these patterns automatically is important for automated map generalisation (Christophe and Ruas, 2002). However, it remains challenging to devise algorithms that can resemble the human level performance. The goal of this paper is to illustrate the potential of graph convolutional networks (GCN) for the identification of patterns and relations important for map generalisation with two use cases: building patterns detection, and road segment selection. Both tasks require some degree of understanding of the spatial relations between map objects. Hence, our experiments constitute a first step in exploring the capability of deep neural network for learning representations of spatial relations. Numéro de notice : C2021-045 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-60-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-60-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99420
Titre : Cluttering reduction for interactive navigation and visualization of historical Images Type de document : Article/Communication Auteurs : Evelyn Paiz-Reyes Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] complexité
[Termes IGN] environnement géographique virtuel
[Termes IGN] exploration d'images
[Termes IGN] image ancienne
[Termes IGN] rendu (géovisualisation)
[Termes IGN] rendu réaliste
[Termes IGN] scène 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Iconographic representations, such as historical photos of geographic spaces, are precious cultural heritage resources capable of describing a particular geographical area’s evolution over time. These photographic collections may vary in size, between hundreds and thousands of items. With the advent of the digital era, many of these documents have been digitized, spatialized, and are available online. Browsing through these digital image collections represents new challenges. This paper examines the topic of historical image exploration in a virtual environment enabling the co-visualization of historical photos into a contemporary 3D scene. We address the topic of user interaction considering the potential volume of the input data. Our methodology is based on design guidelines that rely on visual perception techniques to ease visual complexity and improve saliency on specific cues. The designs are additionally implemented following an image-based rendering approach and evaluated in a group of users. Overall, these propositions may be a notable addition to creating innovative ways to visualize and discover historical images in a virtual geographic environment. Numéro de notice : C2021-048 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-4-81-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-81-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99434
Titre : Content-based image retrieval for map georeferencing Type de document : Article/Communication Auteurs : Jonas Luft, Auteur ; Jochen Schiewe, Auteur Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] appariement d'images
[Termes IGN] carte ancienne
[Termes IGN] carte numérisée
[Termes IGN] carte topographique
[Termes IGN] données localisées des bénévoles
[Termes IGN] géoréférencement indirect
[Termes IGN] mesure de similitude
[Termes IGN] recherche d'image basée sur le contenuRésumé : (auteur) In recent years, libraries have made great progress in digitising troves of historical maps with high-resolution scanners. Providing user-friendly information access for cultural heritage through spatial search and webGIS requires georeferencing of the hundreds of thousands of digitised maps. Georeferencing is usually done manually by finding “ground control points”, locations in the digital map image, whose identity is unambiguous and can easily be found in modern-day reference geodata/mapping data. To decide whether two symbols from different maps describe the same object, their semantic and spatial relations need to be matched. Automating this process is the only feasible way to georeference the immense quantities of maps in conceivable time. However, automated solutions for spatial matching quickly fail when faced with incomplete data – which is the greatest challenge when comparing maps of different ages or scales. These problems can be overcome by computing map similarity in the image domain. Treating maps as a special case of image processing allows efficient and robust matching and thus identification of geographical regions without the need to explicitly model semantics. We propose a method to encode worldwide reference VGI mapping data as image features, allowing the construction of an efficient lookup index. With this index, content-based image retrieval can be used for both geolocating a given map for georeferencing with high accuracy. We demonstrate our approach on hundreds of map sheets of different historical topographical survey map series, successfully georeferencing most of them within mere seconds. Numéro de notice : C2021-073 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Communication DOI : 10.5194/ica-proc-4-69-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-69-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100007
Titre : COVID-19 geoviz for spatio-temporal structures detection Type de document : Article/Communication Auteurs : Jacques Gautier Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Projets : 1-Pas de projet / Gouet-Brunet, Valérie Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] cube espace-temps
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données géographiques
[Termes IGN] maladie virale
[Vedettes matières IGN] GéovisualisationMots-clés libres : Grow Ring Map visualization Résumé : (auteur) The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends. Numéro de notice : C2021-046 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-4-37-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-37-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99398 PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink
