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Hydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])
[article]
Titre : Hydrogeology of the western Po plain (Piedmont, NW Italy) Type de document : Article/Communication Auteurs : Domenico Antonio De Luca, Auteur ; Manuela Lasagna, Auteur ; laura Debernardi, Auteur Année de publication : 2020 Article en page(s) : pp 265 - 273 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] 1:300.000
[Termes IGN] aquifère
[Termes IGN] carte hydrogéologique
[Termes IGN] eau souterraine
[Termes IGN] gestion de l'eau
[Termes IGN] gestion des risques
[Termes IGN] lit majeur
[Termes IGN] Pô (plaine)
[Termes IGN] sédimentRésumé : (auteur) This paper describes the hydrogeological map of the western Po Plain, located in Piedmont (north-western Italy). Po plain represents a hydrogeological system of European relevance, and the Piedmont Plain is the most important groundwater reservoir of the Region. The 1:300,000 scale map was realised using previous and new data to update the knowledge of this area. The map provides information about the hydrogeological complexes and their type and degree of permeability, water table levels and depth, piezometric level fluctuation, lithostratigraphic cross-sections, thickness, and percentage of the permeable deposits between 0 and 50 m from the ground surface. All this information is essential to public administrations, stakeholders, researchers, and professionals for defining possible tools for groundwater protection and management and for planning new groundwater exploitation (i.e. municipal drinking water supplies). Numéro de notice : A2020-646 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2020.1738280 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1080/17445647.2020.1738280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96073
in Journal of maps > vol 16 n° 2 [01/06/2020] . - pp 265 - 273[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)
[article]
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) : n° 338 ; 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:25.000
[Termes IGN] 1:250.000
[Termes IGN] Alpes (France)
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données routières
[Termes IGN] données vectorielles
[Termes IGN] généralisation automatique de données
[Termes IGN] montagne
[Termes IGN] route
[Termes IGN] segmentation
[Termes IGN] symbole graphique
[Termes IGN] virage
[Vedettes matières IGN] GénéralisationRésumé : (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 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) 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
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - n° 338 ; 21 p.[article]Designing multi-scale maps: lessons learned from existing practices / Marion Dumont in International journal of cartography, Vol 6 n° 1 (March 2020)
[article]
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 Projets : MapMuxing / Christophe, Sidonie Article en page(s) : pp 121 - 151 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] carte interactive
[Termes IGN] cognition
[Termes IGN] données multiéchelles
[Termes IGN] échelle cartographique
[Termes IGN] géomatique web
[Termes IGN] niveau d'abstraction
[Termes IGN] niveau de détail
[Termes IGN] représentation multiple
[Termes IGN] Web Map Tile Service
[Termes IGN] zoom
[Vedettes matières IGN] GénéralisationRésumé : (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 Affiliation des auteurs : LASTIG COGIT (2012-2019) 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
in International journal of cartography > Vol 6 n° 1 (March 2020) . - pp 121 - 151[article]Spatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])
[article]
Titre : Spatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland Type de document : Article/Communication Auteurs : Paweł Cybulski, Auteur ; Lukasz Wielebski, Auteur ; Beata Medyńska-Gulij, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 77 - 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] 1:100.000
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte thématique
[Termes IGN] dégradation de l'environnement
[Termes IGN] détection de changement
[Termes IGN] dix-neuvième siècle
[Termes IGN] géoréférencement
[Termes IGN] paysage industriel
[Termes IGN] Pologne
[Termes IGN] prospection minérale
[Termes IGN] système d'information géographique
[Termes IGN] visualisation cartographiqueRésumé : (auteur) The aim of the study is to present landscape changes in the nineteenth century in the central part of the Upper Silesian Industrial District, which is the municipality of Katowice (southern Poland). The comparison of changes, particularly components of the geographical environment, is based on two time periods – the year 1827 and 1883. Nineteenth-century maps were georeferenced, digitized and a series of thematic spatial visualizations presenting quantitative changes were generated by means of the Geographic Information System (GIS). The scale of the visualization created is 1:100,000 and the area is 16,400 ha. The spatial visualization of quantitative landscape change shows the development of the anthropogenic pressure in the form of settlement areas, raw materials extraction places, roads, and the decrease of natural environments, such as forests, rivers, and water bodies. These changes were caused mainly by the exploration of underground deposits and the rapidly growing population of Upper Silesia. Numéro de notice : A2020-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2020.1746416 Date de publication en ligne : 04/04/2020 En ligne : https://doi.org/10.1080/17445647.2020.1746416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96069
in Journal of maps > vol 16 n° 1 [02/01/2020] . - pp 77 - 85[article]Constraint based evaluation of generalized images generated by deep learning / Azelle Courtial (2020)
Titre : Constraint based evaluation of generalized images generated by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Editeur : ICA Commission on Generalisation and Multiple Representation Année de publication : 2020 Projets : 1-Pas de projet / Christophe, Sidonie Conférence : ICA 2020, 23rd Workshop on Map Generalisation and Multiple Representation 05/11/2020 06/11/2020 Delft Pays-Bas Open Access Proceedings Importance : 3 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Français (fre) Descripteur : [Termes IGN] 1:25.000
[Termes IGN] 1:250.000
[Termes IGN] Alpes (France)
[Termes IGN] apprentissage profond
[Termes IGN] carte routière
[Termes IGN] classification pixellaire
[Termes IGN] données maillées
[Termes IGN] généralisation automatique de données
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] montagne
[Termes IGN] précision cartographique
[Termes IGN] programmation par contraintes
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) The use of deep learning techniques for map generalisation raises new problems regarding the evaluation of the results: (1) images are used as input/output instead of vector data; (2) the deep learning processes do not guarantee results that follow cartographic principles; (3) the deep learning models are black boxes that hide the causal mechanisms. Also, deep learning intern evaluation is mostly based on the realism of the images and the pixel classification accuracy, and none of these criteria is sufficient to evaluate a generalisation process. In this article, we propose an adaptation of the constraint-based evaluation to the images generated by deep learning. Six raster-based constraints are proposed for a mountain road generalisation use case. Numéro de notice : C2020-018 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans Date de publication en ligne : 17/11/2020 En ligne : https://varioscale.bk.tudelft.nl/events/icagen2020/ICAgen2020/ICAgen2020_paper_2 [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96380 Géodésie, topographie, cartographie / Bernard Lamy (2020)PermalinkPermalinkA method for drawing vertical curve in longitudinal profile in road project / Hüseyin İnce in Survey review, vol 51 n° 368 (September 2019)PermalinkMultiscale cartographic visualization of harmonized datasets / Peter Kunz in International journal of cartography, vol 5 n° 2-3 (July - November 2019)PermalinkPermalinkFifty shades of Roboto: text design choices and categories in multi-scale maps / Sébastien Biniek (2019)PermalinkPermalinkPermalinkPermalinkLes représentations planes cylindriques de la terre / Françoise Duquenne in XYZ, n° 157 (décembre 2018 - février 2019)PermalinkLarge scale textured mesh reconstruction from mobile mapping images and LIDAR scans / Mohamed Boussaha in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)PermalinkThe development and analysis of quasi-linear map projections / Jonathan Charles Lliffe in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkThe transformation of relief representation on topographic maps in Hungary: from hachures to contour lines / Lazlo Zentai in Cartographic journal (the), vol 55 n° 2 (May 2018)PermalinkRevue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile / Sylvie Daniel in Geomatica, vol 72 n° 1 (March 2018)PermalinkAnalyse de l'évolution des légendes topographiques : Exemple des cartes topographiques IGN et Swisstopo / Jérémie Ory in Cartes & Géomatique, n° 235-236 (mars - juin 2018)PermalinkMaps telling stories ? / Franz-Benjamin Mocnik in Cartographic journal (the), vol 55 n° 1 (February 2018)PermalinkDévelopper un modèle de macro-dynamique forestière pour simuler la dynamique des forêts françaises dans un contexte non-stationnaire / Timothée Audinot (2018)PermalinkDomain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks / Tristan Postadjian (2018)PermalinkQuality control and new data-quality measures for the aesthetics of a Croatian topographic map at the scale of 1:25,000 / Branko Puceković in International journal of cartography, vol 3 n° 2 (December 2017)PermalinkPartial polygon pruning of hydrographic features in automated generalization / Alexander K. Stum in Transactions in GIS, vol 21 n° 5 (October 2017)Permalink