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Experiencing virtual geographic environment in urban 3D participatory e-planning: A user perspective / Thibaud Chassin in Landscape and Urban Planning, vol 224 (August 2022)  

Public [article]  inLandscape and Urban Planning > vol 224 (August 2022) . - n° 104432 Titre : Experiencing virtual geographic environment in urban 3D participatory e-planning: A user perspective Type de document : Article/Communication Auteurs : Thibaud Chassin, Auteur ; Jens Ingensand, Auteur ; Sidonie Christophe , Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° 104432 Note générale : bibliographie This study was partly funded by the Computers & Geosciences Research Scholarships co-sponsored by Elsevier and the International Association for Mathematical Geosciences (IAMG). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche participative [Termes IGN] cognition [Termes IGN] environnement géographique virtuel [Termes IGN] projet urbain [Termes IGN] urbanisme [Termes IGN] utilisateur [Termes IGN] visualisation 3D Résumé : (auteur) The adoption of technology in urban participatory planning with tools such as Virtual Geographic Environments (VGE) promises a broader engagement of urban dwellers, which should ultimately lead to the creation of better cities. However, the authorities and urban experts show hesitancy in endorsing these tools in their practices. Indeed, several parameters must be wisely considered in the design of VGE; if misjudged, their impact could be damaging for the participatory approach and the related urban project. The objective of this study is to engage participants (N = 107) with common tasks conducted in participatory sessions, in order to evaluate the users’ performance when manipulating a VGE. We aimed at assessing three crucial parameters: (1) the VGE representation, (2) the participants’ idiosyncrasies, and (3) the nature of the VGE format. The results demonstrate that the parameters did not affect the same aspect of users’ performance in terms of time, inputs, and correctness. The VGE representation impacts only the time needed to fulfill a task. The participants’ idiosyncrasies, namely age, gender and frequency of 3D use also induce an alteration in time, but spatial abilities seem to impact all characteristics of users’ performance, including correctness. Lastly, the nature of the VGE format significantly alters the time and correctness of users interactions. The results of this study highlight concerns about the inadequacies of the current VGE practices in participatory sessions. Moreover, we suggest guidelines to improve the design of VGE, which could enhance urban participatory planning processes, in order to create better cities. Numéro de notice : A2022-439 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.landurbplan.2022.104432 Date de publication en ligne : 18/04/2022 En ligne : https://doi.org/10.1016/j.landurbplan.2022.104432 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100758 [article]

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)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13 Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] connexité (graphes) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] montagne [Termes IGN] programmation par contraintes [Termes IGN] qualité des données [Termes IGN] rendu réaliste [Termes IGN] route [Vedettes matières IGN] Généralisation Résumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-449 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646 [article]

Neural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 1 (March 2022) . - n° 2031554 ; 19 p. Titre : Neural map style transfer exploration with GANs Type de document : Article/Communication Auteurs : Sidonie Christophe , Auteur ; Samuel Mermet, Auteur ; Morgan Laurent, Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 2031554 ; 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] grille d'échantillonnage [Termes IGN] orthoimage [Termes IGN] représentation cartographique [Termes IGN] réseau antagoniste génératif [Termes IGN] style cartographique [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs. Numéro de notice : A2022-172 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2022.2031554 Date de publication en ligne : 13/02/2022 En ligne : https://doi.org/10.1080/23729333.2022.2031554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99807 [article]

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)  

Public [article]  inInternational journal of cartography > vol 8 n° 1 (March 2022) . - pp 37 - 53 Titre : ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 37 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] compensation par moindres carrés [Termes IGN] données topographiques [Termes IGN] talus [Vedettes matières IGN] Généralisation Résumé : (auteur) While the recent progress on automated generalisation helped National Mapping Agencies to derive topographic maps more and more quickly, there are still practical cartographic issues that require attention. For instance, embankments are represented with line symbols showing the slope of the embankment. This paper proposes an automated algorithm called ReBankment that displaces the embankment lines from the roads and rivers that overlap the embankment symbol. ReBankment is based on a triangulation to identify neighbourhoods, and on a least squares adjustment to displace and distort the embankment line while preserving its shape. This paper also proposes how to handle complex cases and scaling issues. ReBankment is tested on real data from a 1:25k scale topographic map. Numéro de notice : A2022-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2021.1972787 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.1080/23729333.2021.1972787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98838 [article]

AlpineBends – A benchmark for deep learning-based generalisation / Azelle Courtial (2022)  

Public Titre : AlpineBends – A benchmark for deep learning-based generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : 1-Pas de projet / Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] données maillées [Termes IGN] objet géographique [Termes IGN] test de performance [Vedettes matières IGN] Généralisation Résumé : (auteur) [début] Raster-based map generalization is nowadays anecdotal, as most generalization operations are performed using vector data. Vectors describe the shape of each object in the map using a set of coordinates; thus, the object delimitation is directly accessible, and the topology and distance-based relations are easy to compute. On the contrary, rasters represent a map as an image, a grid of pixel covers the target area, and each pixel is characterised by a value. This representation does not explicitly model the boundary/shape of geographic objects and the relations between them. However, the emergence of the image-based deep learning techniques has shown an ability to process images of geographic information. The question of their adaptation for map generalization is a trendy subject: road (Courtial et al. 2020), building (Feng et al. 2019) and coastline (Du et al. 2021) generalization have been explored in recent years. Common methods for evaluating these techniques seems to be necessary for the comparison and development of this field. Numéro de notice : C2021-067 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-1-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-1-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99535

Annotation sémantique pour la géolocalisation d'entités spatiales dans des tweets / Gaëtan Caillaut (2022)  

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Automated construction of a French Entity Linking dataset to geolocate social network posts in the context of natural disasters / Gaëtan Caillaut (2022)  

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BasqueRoads: a benchmark for road network selection / Guillaume Touya (2022)  

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CrossroadsDescriber, automatic textual description of OpenStreetMap intersections / Jérémy Kalsron (2022)  

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Explorer la théorie des ancres et les espaces cognitifs dans la cartographie multi-échelle / Maieul Gruget (2022)  

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Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol 49 n° inconnu (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° inconnu (2022)  

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Representing vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)  

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When is a ring road a ’ring road’? A brief perceptual study / Quentin Potié (2022)  

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