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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]

Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 7 [15/05/2020] . - pp 735 - 758 Titre : Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland Type de document : Article/Communication Auteurs : Izabela Karsznia, Auteur ; Marta Przychodzeń, Auteur ; Karolina Sielicka, Auteur Année de publication : 2020 Article en page(s) : pp 735 - 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] ArcGIS [Termes descripteurs IGN] base de connaissances [Termes descripteurs IGN] base de données orientée objet [Termes descripteurs IGN] bâtiment [Termes descripteurs IGN] données topographiques [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] forêt [Termes descripteurs IGN] placement automatique des objets [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] réseau routier [Vedettes matières IGN] Généralisation Résumé : (auteur) This research presents the formalization and verification of the methodology for the automatic generalization of buildings, road networks, forests and surface waters from the Topographic Objects Database (BDOT10k) in Poland. The article makes the following contributions. First, the generalization methodology contained in the official documents was acquired and presented in the form of the knowledge base. Second, the possibilities and limitations of the implementation of the knowledge base in ArcGIS were discussed. Third, the suitability of the BDOT10k structure for the purpose of automatic generalization performance was verified. As a result of the conducted generalization tests, it was found that the formalization and implementation of the methodology contained in the official specifications, in the automatic mode are not entirely possible. The generalization results, however, are promising. The presented research is in line with the studies recently conducted not only by Polish but also other European National Mapping Agencies. Numéro de notice : A2020-271 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1533591 date de publication en ligne : 03/12/2018 En ligne : https://doi.org/10.1080/10106049.2018.1533591 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95055 [article]

Automated conflation of digital elevation model with reference hydrographic lines / Timofey Samsonov 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) . - 40 p. Titre : Automated conflation of digital elevation model with reference hydrographic lines Type de document : Article/Communication Auteurs : Timofey Samsonov, Auteur Année de publication : 2020 Article en page(s) : 40 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] alignement [Termes descripteurs IGN] cartographie hydrographique [Termes descripteurs IGN] conflation [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réseau de drainage [Termes descripteurs IGN] Triangulated Irregular Network [Vedettes matières IGN] Généralisation Résumé : (auteur) Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation. Numéro de notice : A2020-297 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050334 date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.3390/ijgi9050334 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95135 [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]

Geological map generalization driven by size constraints / Azimjon Sayidov in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 29 p. Titre : Geological map generalization driven by size constraints Type de document : Article/Communication Auteurs : Azimjon Sayidov, Auteur ; Meysam Aliakbarian, Auteur ; Robert Weibel, Auteur Année de publication : 2020 Article en page(s) : 29 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de généralisation [Termes descripteurs IGN] carte géologique [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] généralisation automatique de données [Termes descripteurs IGN] opérateur de généralisation [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] prospection minérale [Termes descripteurs IGN] taille (variable visuelle) [Vedettes matières IGN] Généralisation Résumé : (auteur) Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow. Numéro de notice : A2020-261 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040284 date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.3390/ijgi9040284 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95021 [article]

Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks / Lauwrence V. Stanislawski in International journal of cartography, Vol 6 n° 1 (March 2020)  

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

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Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures / Timofey Samsonov in International journal of cartography, Vol 6 n° 1 (March 2020)  

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Variable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)  

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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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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Comparing historical and contemporary maps : a methodological framework for a cartographic map comparison applied to Swiss maps / Christin Loran in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

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On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

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