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A change of theme: the role of generalization in thematic mapping / Paulo Raposo in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 18 p. Titre : A change of theme: the role of generalization in thematic mapping Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur ; Guillaume Touya , Auteur ; Pia Bereuter, Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] cartogramme [Termes descripteurs IGN] cartographie thématique [Termes descripteurs IGN] couche thématique [Termes descripteurs IGN] fond cartographique [Termes descripteurs IGN] géovisualisation [Termes descripteurs IGN] histoire de la cartographie [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] style cartographique [Termes descripteurs IGN] visualisation cartographique [Vedettes matières IGN] Généralisation Résumé : (auteur) Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography and geovisualization. Generalization is not new to these areas of cartography, and has in fact always been involved in thematic geographic visualization, despite rarely being acknowledged. We illustrate this involvement with several examples of famous, public-audience thematic maps, noting the generalization procedures involved in drawing each, both across their basemap and thematic layers. We also consider, for each map example we note, which generalization operators were crucial to the formation of the map’s thematic message. The many incremental gains made by the cartographic generalization research community while treating reference data can be brought to bear on thematic cartography in the same way they were used implicitly on the well-known thematic maps we highlight here as examples. Numéro de notice : A2020-318 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060371 date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.3390/ijgi9060371 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95183 [article]

Deep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, vol 6 n° 3 (May 2020)  

Public [article]  inACM Transactions on spatial algorithms and systems > vol 6 n° 3 (May 2020) . - 21 p. Titre : Deep learning for enrichment of vector spatial databases: Application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données vectorielles [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] échangeur routier [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] reconnaissance d'objet [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%). Numéro de notice : A2020-365 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3382080 En ligne : https://doi.org/10.1145/3382080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95399 [article]

Exploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 21 p. Titre : Exploring the potential of deep learning segmentation for mountain roads generalisation Type de document : Article/Communication Auteurs : Azelle Courtial, Auteur ; Achraf El Ayedi, Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] 1:25.000 [Termes descripteurs IGN] 1:250.000 [Termes descripteurs IGN] Alpes (France) [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données routières [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] généralisation automatique de données [Termes descripteurs IGN] montagne [Termes descripteurs IGN] route [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] symbole graphique [Termes descripteurs IGN] virage [Vedettes matières IGN] Généralisation Résumé : (auteur) Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Numéro de notice : A2020-295 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050338 date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.3390/ijgi9050338 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95131 [article]

Designing multi-scale maps: lessons learned from existing practices / Marion Dumont in International journal of cartography, Vol 6 n° 1 (March 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 1 (March 2020) Titre : Designing multi-scale maps: lessons learned from existing practices Type de document : Article/Communication Auteurs : Marion Dumont , Auteur ; Guillaume Touya , Auteur ; Cécile Duchêne , Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] carte interactive [Termes descripteurs IGN] cognition [Termes descripteurs IGN] données multiéchelles [Termes descripteurs IGN] échelle cartographique [Termes descripteurs IGN] géomatique web [Termes descripteurs IGN] niveau d'abstraction [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] Web Map Tile Service [Termes descripteurs IGN] zoom [Vedettes matières IGN] Généralisation Résumé : (Auteur) Mapping applications display multi-scale maps where zooming in and out triggers the display of different maps at different scales. Multi-scale maps strongly augmented the potential uses of maps, compared to the traditional single-scaled paper maps. But the exploration of the multi-scale maps can be cognitively difficult for users because the content of the maps can be very different at different scales. This paper seeks to identify the factors in the design of map content and style that increase or decrease the exploration cognitive load, in order to improve multi-scales map design. We studied sixteen existing examples of multi-scale maps to identify these factors that influence a fluid zooming interaction. Several different analyses were conducted on these sixteen multi-scale maps. We first conducted a guided visual exploration of the maps, and a detailed study of the scales of the maps, to identify general trends of good practices (e.g. the WMTS standard that defines zoom levels is widely used) and potential ways of improvement (e.g. a same map is often used at multiple successive zoom levels). Then, we focused on the visual complexity of the multi-scale maps by analyzing how it varies, continuously or not, across scales, using clutter measures, which showed a peak of complexity at zoom level 12 of the WMTS standard. Finally, we studied how buildings and roads are subject to abstraction changes across scales (e.g. at what zoom level individual buildings turn into built-up areas), which can be one of the causes of exploration difficulties. We identified some good practices to reduce the impact of abstraction changes, for instance by mixing different levels of abstraction in the same map. Numéro de notice : A2020-060 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1717832 date de publication en ligne : 28/01/2020 En ligne : https://doi.org/10.1080/23729333.2020.1717832 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94571 [article]

Deep learning for enrichment of vector spatial databases: application to highway interchange / Guillaume Touya (2020)    

Public contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)   Titre : Deep learning for enrichment of vector spatial databases: application to highway interchange Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Editeur : Paris : HAL Année de publication : 2020 Importance : 20 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] conversion de données [Termes descripteurs IGN] données maillées [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] échangeur routier [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] généralisation cartographique [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] segmentation d'image Résumé : (Auteur) Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognise with vector-based techniques. The goal is to find the roads that belong to an interchange, i.e. the slip roads and the highway roads connected to the slip roads. In order to go further than state-of-the-art vector-based techniques, this paper proposes to use raster-based deep learning techniques to recognise highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e. is there an interchange in this image or not?) and image segmentation with a u-net (i.e. find the pixels that cover the interchange) are experimented and give results way better than existing vector-based techniques in this specific use case. Numéro de notice : P2020-001 Thématique : GEOMATIQUE/IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : sans date de publication en ligne : 03/02/2020 En ligne : https://hal.archives-ouvertes.fr/hal-02465244 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94591

Documents numériques

en open access Deep learning for enrichment... - pdf preprint Adobe Acrobat PDF

Inferring the scale and content of a map using deep learning / Guillaume Touya (2020)  

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Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)  

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Is deep learning the new agent for map generalization? / Guillaume Touya in International journal of cartography, vol 5 n° 2-3 (July - November 2019)  

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Automatic derivation of on-demand tactile maps for visually impaired people: first experiments and research agenda / Guillaume Touya in International journal of cartography, vol 5 n° 1 (March 2019)  

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CartAGen: an open source research platform for map generalization / Guillaume Touya (2019)    

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Contour lines generation in karstic plateaus for topographic maps / Guillaume Touya (2019)    

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Enrichissement d'orthophotographie par des données OpenStreetMap pour l'apprentissage machine / Gauthier Fillières-Riveau (2019)    

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Fifty shades of Roboto: text design choices and categories in multi-scale maps / Sébastien Biniek (2019)    

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Finding the oasis in the desert fog? Understanding multi-scale map reading / Guillaume Touya (2019)    

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Le vandalisme dans l’information géographique volontaire, détection de l’IG volontaire vandalisée : du concept à la détection non supervisée d’anomalie / Quy Thy Truong in Revue internationale de géomatique, vol 29 n° 1 (janvier - mars 2019)  

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