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scientifique

Deep learning for anchor detection in multi-scale maps / Quentin Potié (2022)  

Public Titre : Deep learning for anchor detection in multi-scale maps Type de document : Article/Communication Auteurs : Quentin Potié, Auteur ; Guillaume Touya , Auteur ; Chaimaa Beladraoui, Auteur ; Amina El-Moutaouakkil, Auteur ; William A Mackaness, Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 5 Projets : LostInZoom / Touya, Guillaume Conférence : EuroCarto 2022, European Cartographic Conference 19/09/2022 21/09/2022 Vienne Autriche OA Proceedings Note générale : bibliographie This project has received funding from the European Research Council (ERC) under the European Union‟s Horizon 2020 research and innovation programme (grant agreement No. 101003012) Langues : Anglais (eng) Descripteur : [Termes IGN] ancre [Termes IGN] apprentissage profond [Termes IGN] point de repère [Termes IGN] reconnaissance de formes [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Généralisation Numéro de notice : C2022-037 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-5-82-2022 Date de publication en ligne : 14/09/2022 En ligne : http://dx.doi.org/10.5194/ica-abs-5-82-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101622

Complexity-based matching between image resolution and map scale for multiscale image-map generation / Qian Peng in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 10 (October 2021) . - pp 1951 - 1974 Titre : Complexity-based matching between image resolution and map scale for multiscale image-map generation Type de document : Article/Communication Auteurs : Qian Peng, Auteur ; Zhilin Li, Auteur ; Jun Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1951 - 1974 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement de lignes [Termes IGN] échelle cartographique [Termes IGN] Hong-Kong [Termes IGN] niveau de détail [Termes IGN] objet géographique linéaire [Termes IGN] orthophotocarte [Termes IGN] précision cartographique [Termes IGN] précision planimétrique [Termes IGN] résolution globale (imagerie) [Termes IGN] symbole graphique [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Généralisation Résumé : (auteur) An image-map is a compromise between an image and a map. The quality of such maps is affected by several factors, such as (a) the matching between the features on images and the graphic symbols from maps, (b) the complexity of background images, and (c) the representation of graphic and text symbols on the images. This project deals with the first issue. The current solution is that the accuracy of images should satisfy the accuracy standard of maps. However, images with different resolutions can satisfy the standard for a specific map scale. This may lead to a situation in which the levels of detail (LoD) in images may not match the complexity of map features although the planimetric accuracy is matched. To solve this problem, we developed a complexity-based matching between the image resolution and map scale. More precisely, the matching is based on the complexity of line features. Experimental evaluations were conducted in 15 representative areas in Hong Kong using maps at seven scales and eight image resolutions. Results show that the proposed complexity-based method is capable of obtaining good matching between image resolution and map scale in terms of both accuracy and users’ preference. Numéro de notice : A2021-655 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1885674 Date de publication en ligne : 12/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1885674 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98391 [article]

An integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 273 - 295 Titre : An integrated method for DEM simplification with terrain structural features and smooth morphology preserved Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Yifan Zhang, Auteur ; Tinghua Ai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 273 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse structurelle [Termes IGN] arête [Termes IGN] carte géomorphologique [Termes IGN] filtrage statistique [Termes IGN] ligne caractéristique [Termes IGN] limite de terrain [Termes IGN] modèle numérique de surface [Termes IGN] visualisation multiéchelle Résumé : (auteur) As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations. Numéro de notice : A2021-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1772479 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1772479 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96747 [article]

Visual exploration of migration patterns in gull data / Maximilian Konzack in Information visualization, vol 18 n° 1 (January 2019)  

Public [article]  inInformation visualization > vol 18 n° 1 (January 2019) Titre : Visual exploration of migration patterns in gull data Type de document : Article/Communication Auteurs : Maximilian Konzack, Auteur ; Pieter Gijsbers, Auteur ; Ferry Timmers, Auteur ; et al., Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] Aves [Termes IGN] migration animale [Termes IGN] origine - destination [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Géovisualisation Résumé : (auteur) We present a visual analytics approach to explore and analyze movement data as collected by ecologists interested in understanding migration. Migration is an important and intriguing process in animal ecology, which may be better understood through the study of tracks for individuals in their environmental context. Our approach enables ecologists to explore the spatio-temporal characteristics of such tracks interactively. It identifies and aggregates stopovers depending on a scale at which the data is visualized. Statistics of stopover sites and links between them are shown on a zoomable geographic map which allows to interactively explore directed sequences of stopovers from an origin to a destination. In addition, the spatio-temporal properties of the trajectories are visualized by means of a density plot on a geographic map and a calendar view. To evaluate our visual analytics approach, we applied it on a data set of 75 migrating gulls that were tracked over a period of 3 years. The evaluation by an expert user confirms that our approach supports ecologists in their analysis workflow by helping to identifying interesting stopover locations, environmental conditions or (groups of) individuals with characteristic migratory behavior, and allows therefore to focus on visual data analysis. Numéro de notice : A2019-401 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177/1473871617751245 Date de publication en ligne : 20/01/2018 En ligne : https://doi.org/10.1177/1473871617751245 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89120 [article]

Graph mapping: Multi-scale community visualization of massive graph data / David Jonker in Information visualization, vol 16 n° 3 (July 2017)  

Public [article]  inInformation visualization > vol 16 n° 3 (July 2017) Titre : Graph mapping: Multi-scale community visualization of massive graph data Type de document : Article/Communication Auteurs : David Jonker, Auteur ; Scott Langevin, Auteur ; David Giesbrecht, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] graphe [Termes IGN] regroupement de données [Termes IGN] traitement réparti [Termes IGN] visualisation multiéchelle [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this article, we present an end-to-end, tile-based visual analytic approach called graph mapping that utilizes cluster computing to turn large-scale graph (node–link) data into interactive visualizations in modern web browsers. Our approach is designed for end-user analysis of community structure and relationships at macro- and micro scales. We also present the results of several experiments using alternate methods for qualitatively improving comprehensibility of hierarchical community detection visualizations by proposing constraints to state-of-the-art modularity maximization algorithms. Numéro de notice : A2017-758 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177/1473871616661195 En ligne : https://doi.org/10.1177/1473871616661195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89117 [article]