alerte-généralisation
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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)
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[article]
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] GénéralisationRésumé : (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
in International journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 499 - 528[article]Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)
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Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte ancienne
[Termes IGN] cartographie historique
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] données anciennes
[Termes IGN] matrice de co-occurrence
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] vision par ordinateurRésumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804
in ISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211[article]PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
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[article]
Titre : PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes Type de document : Article/Communication Auteurs : Weixiao Gao, Auteur ; Liangliang Nan, Auteur ; Bas Boom, Auteur ; Hugo Ledoux, Auteur Année de publication : 2023 Article en page(s) : pp 32 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de scène 3D
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] contour
[Termes IGN] maillage
[Termes IGN] Perceptron multicouche
[Termes IGN] réseau neuronal de graphes
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic classification. The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes. In the subsequent classification step, we construct a graph that encodes the geometric and photometric features of the segments in its nodes and the multi-scale contextual features in its edges. The final semantic segmentation is obtained by classifying the segments using a graph convolutional network. Experiments and comparisons on two semantic urban mesh benchmarks demonstrate that our approach outperforms the state-of-the-art methods in terms of boundary quality, mean IoU (intersection over union), and generalization ability. We also introduce several new metrics for evaluating mesh over-segmentation methods dedicated to semantic segmentation, and our proposed over-segmentation approach outperforms state-of-the-art methods on all metrics. Our source code is available at https://github.com/WeixiaoGao/PSSNet. Numéro de notice : A2023-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.020 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102399
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 32 - 44[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)
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[article]
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] GénéralisationRésumé : (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
in ISPRS International journal of geo-information > vol 12 n° 2 (February 2023) . - n° 62[article]
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
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AnchorWhat - pdf auteurAdobe Acrobat PDFImproving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)
PermalinkIncorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (2023)
PermalinkMissing 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)
PermalinkPermalinkAssessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])
PermalinkA comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)
PermalinkInstance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
PermalinkProgressive 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)
PermalinkAn unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)
PermalinkEvaluation 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)
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