Centre de documentation
scientifique

Deriving map images of generalised mountain roads with generative adversarial networks / Azelle Courtial in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 499 - 528 Titre : Deriving map images of generalised mountain roads with generative adversarial networks Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2023 Article en page(s) : pp 499 - 528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage non-dirigé [Termes IGN] carte routière [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] généralisation cartographique automatisée [Termes IGN] montagne [Termes IGN] réseau antagoniste génératif [Vedettes matières IGN] Généralisation Résumé : (auteur) Map generalisation is a process that transforms geographic information for a cartographic at a specific scale. The goal is to produce legible and informative maps even at small scales from a detailed dataset. The potential of deep learning to help in this task is still unknown. This article examines the use case of mountain road generalisation, to explore the potential of a specific deep learning approach: generative adversarial networks (GAN). Our goal is to generate images that depict road maps generalised at the 1:250k scale, from images that depict road maps of the same area using un-generalised 1:25k data. This paper not only shows the potential of deep learning to generate generalised mountain roads, but also analyses how the process of deep learning generalisation works, compares supervised and unsupervised learning and explores possible improvements. With this experiment we have exhibited an unsupervised model that is able to generate generalised maps evaluated as good as the reference and reviewed some possible improvements for deep learning-based generalisation, including training set management and the definition of a new road connectivity loss. All our results are evaluated visually using a four questions process and validated by a user test conducted on 113 individuals. Numéro de notice : A2023-073 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2123488 Date de publication en ligne : 20/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2123488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101901 [article]

Where am I now? modelling disorientation in pan-scalar maps / Guillaume Touya in ISPRS International journal of geo-information, vol 12 n° 2 (February 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 2 (February 2023) . - n° 62 Titre : Where am I now? modelling disorientation in pan-scalar maps Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Maieul Gruget , Auteur ; Ian Muehlenhaus, Auteur Année de publication : 2023 Article en page(s) : n° 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cognition [Termes IGN] données multiéchelles [Termes IGN] échelle cartographique [Termes IGN] interaction homme-machine [Termes IGN] lecture de carte [Termes IGN] représentation mentale [Termes IGN] représentation mentale spatiale [Termes IGN] représentation multiple [Vedettes matières IGN] Généralisation Résumé : (auteur) Disorientation is a common feeling for all users of zoomable multi-scale maps, even for those with good orientation and spatial skills. We make the assumption that this problem is mainly due to the desert fog effect, documented in human–computer interaction within multi-scale zoomable environments. Starting with a collection of reported experiences of disorientation, this paper explores this notion from the spatial cognition, philosophical and human–computer interaction perspectives and proposes a model of disorientation in the exploration of multi-scale maps. We argue that disorientation is a problem of reconciliation between the current map view and the mental map of the user, where landmarks visible on the map or memorised in the mental map play a key role. The causes for failed reconciliation are discussed and illustrated by our collected experiences of disorientation. Numéro de notice : A2023-130 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12020062 En ligne : https://doi.org/10.3390/ijgi12020062 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102585 [article]

AnchorWhat : Décompositions de cartes pan-scalaires / Maieul Gruget (2023) 

Public Titre : AnchorWhat : Décompositions de cartes pan-scalaires Type de document : Article/Communication Auteurs : Maieul Gruget , Auteur ; Guillaume Touya , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2023 Conférence : Journée Recherche de l'UGE-IGN-ENSG 2023, 32e journée de la recherche, Jumeaux numérique et anthropocène : données de simulation pour aider à la prise de décision 30/03/2023 Champs-sur-Marne France programme Langues : Français (fre) Descripteur : [Vedettes matières IGN] Généralisation Résumé : (auteur) Poster de vulgarisation scientifique à la journée de la recherche IGN 2023. Inspiré de la théorie des ancres de Couclelis et collègues, ce poster présente une méthodologie d'analyse de présence et persistance d'éléments cartographiques à travers différentes explorations cartographiques. Numéro de notice : C2023-002 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Poster nature-HAL : Poster-sans-CL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103185

Documents numériques

peut être téléchargé AnchorWhat - pdf auteur Adobe Acrobat PDF

Exploring the potential of deep learning for map generalization / Azelle Courtial (2023)  

Public Titre : Exploring the potential of deep learning for map generalization Type de document : Thèse/HDR Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Directeur de thèse ; Xiang Zhang, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2023 Importance : 216 p. Note générale : bibliographie Doctoral thesis from Université Gustave Eiffel, Doctoral school MSTIC, Specialty "Geographic information sciences" Langues : Anglais (eng) Descripteur : [Termes IGN] généralisation automatique de données [Termes IGN] généralisation cartographique automatisée [Termes IGN] relation spatiale [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal profond [Vedettes matières IGN] Généralisation Index. décimale : THESE Thèses et HDR Résumé : (auteur) Map generalization is a process that aims to adapt the level of detail of geographic information for cartography at a small scale. Automating the process is complex but essential in map production. We think this research field could benefit from the recent advances in deep learning that make it possible to solve more and more complex tasks, using numerous training examples. This thesis proposes exploring the potential of deep learning for map generalization. This exploration is built upon three map generalization use cases: recognition of spatial relations, graphic generalization of mountain roads, and generalization of topographic maps at medium scales. These three use cases enable us to address research questions relative to the concrete implementation of deep learning models for map generalization (including dataset creation and architecture), the evaluation of such models and their integration in existing generalization processes. In addition to the models and training set adapted for each of our case studies already mentioned, we propose evaluation methods adapted to the challenges of cartographic generalization by deep learning. Finally, we propose a partitioning of the cartographic generalization into sub-problems facilitating the resolution by learning and allowing the generation of generalized map images. Note de contenu : Introduction Part 1 A new paradigm for map generalization Chapter A. Literature review Chapter B. Formulating map generalization as a deep learning task Chapter C. Designing a framework for deep learning based map generalization Part 2 Exploration of deep learning for map generalization Chapter D. Can graph neural networks model spatial relations? Chapter E. CNN for the generalization of roads Chapter F. The generation of topographic map with several themes Part III The future of map generalization with deep learning Chapter G. Usages of deep learning models for map generalization Chapter H. Evaluation of deep learning predictions Conclusion Numéro de notice : 17752 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Thèse française Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans Date de publication en ligne : 05/05/2023 En ligne : https://theses.hal.science/tel-04089883v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103186

A hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 1 (January 2023) Titre : A hexagon-based method for polygon generalization using morphological operators Type de document : Article/Communication Auteurs : Lu Wang, Auteur ; Tinghua Ai, Auteur ; Dirk Burghardt, Auteur ; Yilang Shen, Auteur ; Min Yang, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données maillées [Termes IGN] généralisation cartographique automatisée [Termes IGN] morphologie mathématique [Termes IGN] polygone [Vedettes matières IGN] Généralisation Résumé : (auteur) Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins. Numéro de notice : A2023-071 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2108036 Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2108036 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101387 [article]

Incorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (2023)  

Permalink

Missing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps / Maieul Gruget in International journal of cartography, vol inconnu (2023)  

Permalink

Progressive collapse of dual-line rivers based on river segmentation considering cartographic generalization rules / Fubing Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

Permalink

An unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

Permalink

Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)  

Permalink

Semi-automatic development of thematic tactile maps / Jakub Wabiński in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)  

Permalink

Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Permalink

Segmentation and sampling method for complex polyline generalization based on a generative adversarial network / Jiawei Du in Geocarto international, vol 37 n° 14 ([20/07/2022])  

Permalink

Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Permalink

Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)  

Permalink