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Titre : AlpineBends – A benchmark for deep learning-based generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya
, Auteur ; Xiang Zhang, Auteur
Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : 1-Pas de projet / Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] données maillées
[Termes IGN] objet géographique
[Termes IGN] test de performance
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [début] Raster-based map generalization is nowadays anecdotal, as most generalization operations are performed using vector data. Vectors describe the shape of each object in the map using a set of coordinates; thus, the object delimitation is directly accessible, and the topology and distance-based relations are easy to compute. On the contrary, rasters represent a map as an image, a grid of pixel covers the target area, and each pixel is characterised by a value. This representation does not explicitly model the boundary/shape of geographic objects and the relations between them. However, the emergence of the image-based deep learning techniques has shown an ability to process images of geographic information. The question of their adaptation for map generalization is a trendy subject: road (Courtial et al. 2020), building (Feng et al. 2019) and coastline (Du et al. 2021) generalization have been explored in recent years. Common methods for evaluating these techniques seems to be necessary for the comparison and development of this field. Numéro de notice : C2021-067 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-1-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-1-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99535
Titre : BasqueRoads: a benchmark for road network selection Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Azelle Courtial
, Auteur
Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 4 Projets : LostInZoom / Touya, Guillaume Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Note générale : bibliographie
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101003012).Langues : Anglais (eng) Descripteur : [Termes IGN] objet géographique
[Termes IGN] réseau routier
[Termes IGN] simplification de contour
[Termes IGN] test de performance
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [début] Road network selection is one of the major issues of map generalisation, as new papers are proposed every year since the first attempts of automation in the 1990’s (Thomson & Richardson, 1995). New methods are regularly proposed because selecting roads for maps at smaller scales is a complex problem. Roads are at the same time present in maps to enable car navigation tasks, and because they are structuring elements that reveal the nature of the landscape (urban, rural, mountainous…). So road selection is not only about retaining the most important roads of the network, but the preservation of topology and connectivity is essential, as well as the preservation, or the typification of road patterns (e.g. a ring road), and the preservation of local density differences (between urban and rural areas for instance). It is rare to see comparisons of road selection techniques in the literature, because of the lack of open source in map generalisation, but also because of the lack of a common dataset to benchmark these techniques; new propositions on road selection are most of the time tied to their own dataset and use case. This is why we think that this BasqueRoads dataset could be useful to advance on this topic of road network selection. Numéro de notice : C2021-066 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-4-5-2022 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.5194/ica-abs-4-5-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99536
Titre : L’Alpe d’Huez: A benchmark for topographic map generalisation Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur
Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Conférence : ICA 2021, 24th ICA Workshop on Map Generalisation and Multiple Representation 13/12/2021 13/12/2021 Florence Italie OA Proceedings Importance : 2 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] bâtiment
[Termes IGN] CartAGen (plateforme de généralisation)
[Termes IGN] carte topographique
[Termes IGN] généralisation cartographique
[Termes IGN] Institut national de l'information géographique et forestière (France)
[Termes IGN] test de performance
[Vedettes matières IGN] GénéralisationRésumé : (auteur) L’Alpe d’Huez is a ski resort in the French Alps, and it is also famous among cyclists for the number of bends in the road to the ski resort. It is a good location to evaluate the capabilities of map generalisation tools, as the surroundings contain urban, rural and mountainous areas, and it was chosen 15 years ago as one of the four datasets to benchmark map generalisation software (Stoter et al., 2009). The EuroSDR benchmark used data from IGN France, the French National Mapping Agency (NMA). At that time, open science policies were not popular in NMAs, but now they release their dataset with open licenses, so it is a good opportunity to create an open benchmark for topographic map generalisation. Numéro de notice : C2021-065 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://hal.archives-ouvertes.fr/hal-03522475/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99524 An attempt to define perceptive and sensitive mapping through lived space experiments / Catherine Dominguès (2021)
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Titre : An attempt to define perceptive and sensitive mapping through lived space experiments Type de document : Article/Communication Auteurs : Catherine Dominguès , Auteur ; Laurence Jolivet
, Auteur ; Eric Mermet
, Auteur ; Sevil Seten, Auteur
Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA num. 3 Projets : 1-Pas de projet / Touya, Guillaume Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie OA Proceedings Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des besoins
[Termes IGN] cartographie sensible
[Termes IGN] expérience scientifique
[Termes IGN] utilisateur
[Vedettes matières IGN] CartologieRésumé : (auteur) [début] Maps are often used in the context of human and social sciences, including as a tool. For example, maps as graphic tools enable to locate survey fields and data. Especially, the synoptic property of maps makes it possible to investigate the spatial dimension of a phenomenon, the distribution of data, its changes over time, etc. In teaching activities and in support tasks for research at the EHESS in Paris, difficulties have arisen in showing research data and results in a manner which would be fruitful and acceptable to the students and researchers. The need for an adapted mapping has emerged, including the map-making process and the achieved map. Adapted mapping has been named by the phrase perceptive and sensitive mapping, in contrast with conventional mapping based on geographical databases, GIS tools and the theory of graphic semiology as taught by Jacques Bertin (Bertin, 1983). In response to this need, a training methodological seminar has been set up since 2016 in EHESS. It aims at providing an (organizational and material) framework for students in which they can experiment various protocols and be confronted with different data specifications. The procedures are designed in order to accentuate specific aspects that are not supposed to be fulfilled by conventional mapping. An analysis has been performed targeting the students' achievements and how they have been achieved. The analysis makes it possible to characterize the maps drawn in this context; to compare the students' difficulties and comments with the needs they initially expressed; to highlight in which cases conventional cartography may be inadequate for laying out some data. The result analysis enabled considering three questions: how may conventional mapping and perceptive and sensitive mapping be compared? How is perceptive and sensitive mapping a relevant tool? And thanks to the answers of the previous questions: What would be a definition of perceptive and sensitive mapping? To this end, the paper firstly details how the needs for maps were expressed and how the seminar tried to answer them by defining experiments. In the second section, the achievements are analyzed based on two items: the (displayed) graphical and cartographic features, and the protocols which enabled to make them. Lastly, the analysis enables to offer a definition of perceptive and sensitive mapping by means of a comparison with conventional mapping. Numéro de notice : C2021-044 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-70-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-70-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99394
Titre : Can graph convolution networks learn spatial relations? Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya
, Auteur ; Xiang Zhang, Auteur
Editeur : ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Abstracts of the ICA num. 3 Projets : 1-Pas de projet / Touya, Guillaume Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie OA Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] alignement
[Termes IGN] bati
[Termes IGN] objet géographique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (auteur) [introduction] Maps are composed of spatially related geographic objects. Spatial relations are key information for human as they support the description of relative locations: the house is to the east of the city centre, near the interchange, or at the end of the path. Consequently, preserving these spatial relations is important during map generalisation. For example, building typification is a generalisation operation that seeks to reduce the quantity of building while preserving relation between and within homogeneous buildings groups (Regnauld, 2001). Building or road patterns are remarkable distributions of elements in the map from which high-level concepts and semantics (e.g. landuse types and urban morphology) can be inferred. Such patterns can be characterized by spatial relations (e.g. proximity, similarity and continuity of these elements) and hence are visually easy to identify by a human. To identify these patterns automatically is important for automated map generalisation (Christophe and Ruas, 2002). However, it remains challenging to devise algorithms that can resemble the human level performance. The goal of this paper is to illustrate the potential of graph convolutional networks (GCN) for the identification of patterns and relations important for map generalisation with two use cases: building patterns detection, and road segment selection. Both tasks require some degree of understanding of the spatial relations between map objects. Hence, our experiments constitute a first step in exploring the capability of deep neural network for learning representations of spatial relations. Numéro de notice : C2021-045 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-3-60-2021 Date de publication en ligne : 13/12/2021 En ligne : https://doi.org/10.5194/ica-abs-3-60-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99420 Cluttering reduction for interactive navigation and visualization of historical Images / Evelyn Paiz-Reyes (2021)
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PermalinkPermalinkPermalinkMapping and characterizing animals’ places of interest in forest environment / Laurence Jolivet (2021)
PermalinkPermalinkModelling and building of a graph database of multi-source landmarks to help emergency mountain rescuers / Véronique Gendner (2021)
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