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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]

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)  

Public [article]  inInternational journal of cartography > vol inconnu (2023) Titre : Incorporating ideas of structure and meaning in interactive multi scale mapping environments Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Quentin Potié , Auteur ; William A Mackaness, Auteur Année de publication : 2023 Projets : LostInZoom / Touya, Guillaume Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] état de l'art [Termes IGN] généralisation cartographique automatisée [Termes IGN] lisibilité perceptive [Termes IGN] reconnaissance de formes [Termes IGN] web mapping [Vedettes matières IGN] Généralisation Résumé : (auteur) Web based, slippy, scalable maps are common place. Interacting with such digital maps at varying levels of detail is key to interpretation, and exploration of different geographies. The process of abstraction remains key to the immediate and successful interpretation of their many structures and geographical associations found at any given scale. Meaning is derived from such recognisable structures and map generalisation plays a critical role in communicating an entity's most characteristic and salient qualities. But what are these structures? How (and why) do they change over scale? Why are such questions relevant to automated mapping? In this paper we reflect on the value of perceptual studies and reconsider the context in which map generalisation now takes place. We review developments in pattern recognition techniques and the role played by machine learning techniques in identifying high level structures in abstracted maps. The benefits of their application include derivation of ontological descriptions of landscape, identification and preservation of salient landmarks across scales. We argue that a 'structuralist based approach' provides a more meaningful basis for measuring success and achieving more meaningful outputs. Ultimately the ambition is greater levels of automation in map generalisation, particularly in the context of web based solutions. Numéro de notice : A2023-099 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2023.2215960 Date de publication en ligne : 01/06/2023 En ligne : https://doi.org/10.1080/23729333.2023.2215960 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103273 [article]

Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte topographique [Termes IGN] construction [Termes IGN] décomposition [Termes IGN] détection du bâti [Termes IGN] forme linéaire [Termes IGN] généralisation cartographique automatisée [Termes IGN] Ordnance Survey (UK) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260 [article]

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

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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)  

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Procedural urban forestry / Till Niese in ACM Transactions on Graphics, TOG, Vol 41 n° 2 (April 2022)  

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Accessing spatial knowledge networks with maps / Markus Jobst in International journal of cartography, vol 8 n° 1 (March 2022)  

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Identification de relations spatiales par apprentissage profond sur des graphes / Azelle Courtial in Cartes & Géomatique, n° 247-248 (mars-juin 2022)

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ReBankment : un algorithme pour déplacer les talus sur les cartes par moindres carrés / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)

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ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment / Guillaume Touya in International journal of cartography, vol 8 n° 1 (March 2022)  

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Guidelines for standardizing the design of tactile maps: A review of research and best practice / Jakub Wabiński in Cartographic journal (the), vol 59 n° 3 (August 2022)  

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

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Multi-criteria geographic analysis for automated cartographic generalization / Guillaume Touya in Cartographic journal (the), vol 59 n° 1 (February 2022)  

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