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Auteur Azelle Courtial
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Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesExploring 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)
Titre : Exploring the potential of deep learning segmentation for mountain roads generalisation Type de document : Article/Communication Auteurs : Azelle Courtial 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]
Titre : Constraint based evaluation of generalized images generated by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial Editeur : ICA Commission on Generalisation and Multiple Representation Année de publication : 2020 Projets : 1-Pas de projet / 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
Titre : Inferring the scale and content of a map using deep learning Type de document : Article/Communication Auteurs : Guillaume Touya Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLIII-B4 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : ISPRS 2020, Commission 4, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 4 Importance : pp 17 - 24 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] carte numérisée
[Termes IGN] carte scolaire
[Termes IGN] carte tactile
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échelle cartographique
[Termes IGN] formation
[Termes IGN] généralisation
[Termes IGN] géographie physique
[Termes IGN] individu non-voyantRésumé : (auteur) Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. "there are roads, cities and administrative boundaries"), and to infer the extent of the map (e.g. a map of France or of Europe). Numéro de notice : C2020-002 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B4-2020-17-2020 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-17-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95391
