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Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 313 - 337 Titre : Polyline simplification based on the artificial neural network with constraints of generalization knowledge Type de document : Article/Communication Auteurs : Jiawei Du, Auteur ; Jichong Yin, Auteur ; Chengyi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 313 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] descripteur [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] généralisation cartographique automatisée [Termes IGN] polyligne [Termes IGN] programmation par contraintes [Termes IGN] réseau neuronal artificiel [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles. Numéro de notice : A2022-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2021.2013944 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100885 [article]

Procedural urban forestry / Till Niese in ACM Transactions on Graphics, TOG, Vol 41 n° 2 (April 2022)  

Public [article]  inACM Transactions on Graphics, TOG > Vol 41 n° 2 (April 2022) . - pp 1 - 18 Titre : Procedural urban forestry Type de document : Article/Communication Auteurs : Till Niese, Auteur ; Sören Pirk,, Auteur ; Matthias Albrecht,, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] arbre urbain [Termes IGN] paysage urbain [Termes IGN] placement automatique des objets [Termes IGN] scène urbaine [Termes IGN] scène virtuelle Résumé : (auteur) The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users. Numéro de notice : A2022-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1145/3502220 En ligne : https://doi.org/10.1145/3502220 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100371 [article]

ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment / Guillaume Touya in International journal of cartography, vol 8 n° 1 (March 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 1 (March 2022) . - pp 37 - 53 Titre : ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 37 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] compensation par moindres carrés [Termes IGN] données topographiques [Termes IGN] talus [Vedettes matières IGN] Généralisation Résumé : (auteur) While the recent progress on automated generalisation helped National Mapping Agencies to derive topographic maps more and more quickly, there are still practical cartographic issues that require attention. For instance, embankments are represented with line symbols showing the slope of the embankment. This paper proposes an automated algorithm called ReBankment that displaces the embankment lines from the roads and rivers that overlap the embankment symbol. ReBankment is based on a triangulation to identify neighbourhoods, and on a least squares adjustment to displace and distort the embankment line while preserving its shape. This paper also proposes how to handle complex cases and scaling issues. ReBankment is tested on real data from a 1:25k scale topographic map. Numéro de notice : A2022-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2021.1972787 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.1080/23729333.2021.1972787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98838 [article]

Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 37 n° inconnu ([25/01/2022])  

Public [article]  inGeocarto international > vol 37 n° inconnu [25/01/2022] Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte topographique [Termes IGN] construction [Termes IGN] décomposition [Termes IGN] détection du bâti [Termes IGN] forme linéaire [Termes IGN] généralisation cartographique automatisée [Termes IGN] Ordnance Survey (UK) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260 [article]

Is map generalisation a computational cartography problem? / Guillaume Touya (2022)  

Public Titre : Is map generalisation a computational cartography problem? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2022 Projets : LostInZoom / Touya, Guillaume Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] généralisation cartographique automatisée [Termes IGN] géomètrie algorithmique [Vedettes matières IGN] Généralisation Résumé : (auteur) Map generalisation is one of the processes of map design, when the spatial data used to make the map is too detailed for the scale of the map. Map generalisation seeks to abstract and simplify this detailed spatial information to represent it on the map with a good legibility. Map generalisation involves different types of transformations: selection, simplification, displacement, aggregation, collapse, etc. Historically, computational geometry researchers greatly contributed to the automation of map generalisation by proposing algorithms to achieve some of these atomic transformations. But the research questions addressed by researchers on map generalisation have changed over the years, with the progress on the automation, and the changes in the way people use maps (apparition of multi-scale zoomable web maps). Is map generalisation still a computational cartography problem? To discuss this rhetorical question, this presentation illustrates the current issues of map generalisation, with past contribution of computational cartography and possible computational cartography problems. For instance, there are still needs for new specific algorithms, either because maps are much more diverse, or because the larger scale range calls for new transformations. As maps tend to mix elements of topographic maps and elements of schematized maps, computational cartography is also relevant to solve this task. Finally, as the current trend in map generalisation is the use of deep learning models, the presentation discusses the relevance of computational cartography techniques in this context. Numéro de notice : C2022-028 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Conférence invitée nature-HAL : Conf-Invitée DOI : sans En ligne : https://hal.archives-ouvertes.fr/IGN-ENSG/hal-03677309v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100959

Multi-criteria geographic analysis for automated cartographic generalization / Guillaume Touya in Cartographic journal (the), vol 59 n° inconnu (2022)  

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Génération automatique de courbes de niveaux dans les zones de plateaux karstiques / Guillaume Touya in Cartes & Géomatique, n° 243-244 (mars - juin 2021)

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Aggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

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Generative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)  

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Cartographic generalization / Monika Sester in Journal of Spatial Information Science (JoSIS), n° 21 (2020)  

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A multi-scale representation model of polyline based on head/tail breaks / Pengcheng Liu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

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Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

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Mapping the French green infrastructure – an exercise in homogenizing heterogeneous regional data / Lucille Billon in International journal of cartography, Vol 6 n° 2 (July 2020)  

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Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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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])  

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