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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 / 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 : Exact optimization algorithms for the aggregation of spatial data Type de document : Thèse/HDR Auteurs : Johannes Oehrlein, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 862 Importance : 184 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation zur Erlangung des GradesDoktor-Ingenieur (Dr.-Ing.)
Diese Arbeit ist gleichzeitig als elektronische Dissertationbei der Universitäts-und Landesbibliothek Bonn veröffentlichtLangues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale
[Termes IGN] cycliste
[Termes IGN] données localisées
[Termes IGN] espace vert
[Termes IGN] généralisation automatique de données
[Termes IGN] programmation linéaire
[Termes IGN] réseau routier
[Termes IGN] trajet (mobilité)
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The aggregation of spatial data is a recurring problem in geoinformation science. Aggregating data means subsuming multiple pieces of information into a less complex representation. It is pursued for various reasons, like having a less complex data structure to apply further processing algorithms or a simpler visual representation as targeted in map generalization. In this thesis, we identify aggregation problems dealing with spatial data and formalize themas optimization problems. That means we set up a function that is capable of evaluating valid solutions to the considered problem, like a cost function for minimization problems. To each problem introduced, we present an algorithm that finds a valid solution that optimizes this objective function. In general, this superiority with respect to the quality of the solution comes at the cost of computation efficiency, a reason why non-exact approaches like heuristics are widely used for optimization. Nevertheless, the higher quality of solutions yielded by exact approaches is undoubtedly important. On the one hand, “good” solutions are sometimes not sufficient. On the other hand, exact approaches yield solutions that maybe used as benchmarks for the evaluation of non-exact approaches. This kind of application is of particular interest since heuristic approaches, for example, give no guarantee on the quality of solutions found. Furthermore, algorithms that provide exact solutions to optimization problems reveal weak spots of underlying models. A result that does not satisfy the user cannot be excused with a mediocre performance of an applied heuristic. With this motivation, we developed several exact approaches for aggregation problems, which we present in this thesis. Since we deal with spatial data, for all problems considered, the aggregation is based on both geometric and semantic aspects although the focus varies. The first problem we discuss is about visualizing a road network in the context of navigation. Given a fixed location in the network, we aim for a clear representation of the surroundings. For this purpose, we introduce an equivalence relation for destinations in the network based on which we perform the aggregation. We succeed in designing an efficient algorithm that aggregates as many equivalent destinations as possible. Furthermore, we tackle a class of similar and frequently discussed problems concerning the aggregation of areal units into larger, connected regions. Since these problems are NP-complete, i.e. extraordinarily complex, we do not aim for an efficient exact algorithm (which is suspected not to exist) but present a strong improvement to existing exact approaches. In another setup, we present an efficient algorithm for the analysis of urban green-space supply. Performing a hypothetical assignment of citizens to available green spaces, it detects local shortages and patterns in the accessibility of green space within a city. Finally, we introduce and demonstrate a tool for detecting route preferences of cyclists based on a selection of given trajectories. Examining a set of criteria forming suitable candidates, we aggregate them efficiently to the best-fitting derivable criterion. Overall, we present exact approaches to various aggregation problems. In particular, the NP-complete problem we deal with firmly underscores, as expected, the need for heuristic approaches. For applications asking for an immediate solution, it may be reasonable to apply a heuristic approach. This holds in particular due to easy and generally applicable meta-heuristics being available. However, with this thesis, we argue for applying exact approaches if possible. The guaranteed superior quality of solutions speaks for itself. Besides, we give additional examples which show that exact approaches can be applied efficiently as well. Numéro de notice : 17681 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD dissertation : : Rheinische Friedrich-Wilhelms-Universität Bonn : 2020 En ligne : https://nbn-resolving.org/urn:nbn:de:hbz:5-60713 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98023 MapGenOnto: A shared ontology for map generalisation and multi-scale visualisation / Guillaume Touya (2020)
Titre : MapGenOnto: A shared ontology for map generalisation and multi-scale visualisation Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur 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 : Anglais (eng) Descripteur : [Termes IGN] généralisation automatique de données
[Termes IGN] ontologie
[Termes IGN] plateforme collaborative
[Termes IGN] visualisation de données
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The usefulness of ontologies for map generalisation and on-demand mapping has been acknowledged by the research community for now more than ten years. But past attempts to build an ontology that shares the conceptualisation views of the community have fell short for now, maybe due to a lack of direct use cases. MapGenOnto is a new attempt to gather researchers around a shared ontology that covers the description of the geography and the map, and also the generalisation processes used to generalise this map. This short paper briefly describes the backbone concepts of this ontology, and then presents a use case to describe cross-platform ScaleMaster2.0 specifications. Numéro de notice : C2020-017 Affiliation des auteurs : UGE-LASTIG (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_1 [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96318 Is deep learning the new agent for map generalization? / Guillaume Touya in International journal of cartography, vol 5 n° 2-3 (July - November 2019)
[article]
Titre : Is deep learning the new agent for map generalization? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Xiang Zhang, Auteur ; Imran Lokhat , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ICC 2019, 29th International Cartographic Conference ICA, Mapping everything for everyone 15/07/2019 20/07/2019 Tokyo Japon Open Access Proceedings of the ICA Article en page(s) : pp 142 - 157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] système multi-agents
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) The automation of map generalization has been keeping researchers in cartography busy for years. Particularly great progress was made in the late 90s with the use of the multi-agent paradigm. Although the current use of automatic processes in some national mapping agencies is a great achievement, there are still many unsolved issues and research seems to stagnate in the recent years. With the success of deep learning in many fields of science, including geographic information science, this paper poses the controversial question of the title: is deep learning the new agent, i.e. the technique that will make generalization research bridge the gap to fully automated generalization processes? The paper neither responds a clear yes nor a clear no but discusses what issues could be tackled with deep learning and what the promising perspectives. Some preliminary experiments with building generalization or data enrichments are presented to support the discussion. Numéro de notice : A2019-235 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1613071 Date de publication en ligne : 09/05/2019 En ligne : https://doi.org/10.1080/23729333.2019.1613071 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92932
in International journal of cartography > vol 5 n° 2-3 (July - November 2019) . - pp 142 - 157[article]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)
[article]
Titre : Automatic derivation of on-demand tactile maps for visually impaired people: first experiments and research agenda Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Sidonie Christophe , Auteur ; Jean-Marie Favreau, Auteur ; Mohamed Amine Ben Rhaiem, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 67 - 91 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] automatisation
[Termes IGN] carte sur mesure
[Termes IGN] carte tactile
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] impression 3D
[Termes IGN] personne malvoyante
[Termes IGN] style cartographique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Tactile maps are essential tools for visually impaired people to comprehend space and to support the simple pedestrian trips made difficult by their disability. Tactile maps are created manually and printed by specialists, and it takes a large amount of time to create a new one, which prevents using them on demand for everyday use. As a consequence, researchers and cartographers try to automate this creation process, but the existing automated derivation processes do not include generalization or advanced stylization steps, which limits their effectiveness. This paper reports first experiments to include such complex automated cartography processes to provide on-demand tactile maps for visually impaired people. These first experiments were more intended to raise real research issues than solve them, and the paper discusses these issues in a research agenda to achieve automatically derived tactile maps. Numéro de notice : A2019-379 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2018.1486784 Date de publication en ligne : 07/08/2018 En ligne : https://doi.org/10.1080/23729333.2018.1486784 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90797
in International journal of cartography > vol 5 n° 1 (March 2019) . - pp 67 - 91[article]PermalinkPermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)PermalinkTeragons for testing implementations of point reduction algorithms / Mahes Visvalingam in Cartographic journal (the), Vol 55 n° 3 (August 2018)PermalinkComplexity reduction in choropleth map animations by autocorrelation weighted generalization of time-series data / Christoph Traun in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkMeasured and perceived visual complexity : a comparative study among three online map providers / Susan Schnur in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkRecognizing building groups for generalization : a comparative study / Min Deng in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkLabelling hierarchy for street maps using centrality measures / Wasim Shoman in Cartographic journal (the), vol 55 n° 1 (February 2018)PermalinkA typification method for linear pattern in urban building generalisation / Xianyong Gong in Geocarto international, vol 33 n° 2 (February 2018)PermalinkGénéralisation de représentations intermédiaires dans une carte topographique multi-échelle pour faciliter la navigation de l’utilisateur / Marion Dumont (2018)Permalink