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Segmentation and sampling method for complex polyline generalization based on a generative adversarial network / Jiawei Du in Geocarto international, vol 37 n° 14 ([20/07/2022])  

Public [article]  inGeocarto international > vol 37 n° 14 [20/07/2022] . - pp 4158 - 4180 Titre : Segmentation and sampling method for complex polyline generalization based on a generative adversarial network Type de document : Article/Communication Auteurs : Jiawei Du ; Fang Wu, Auteur ; Ruixing Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4158 - 4180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage de données [Termes IGN] implémentation (informatique) [Termes IGN] polyligne [Termes IGN] rastérisation [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation [Vedettes matières IGN] Généralisation Résumé : (auteur) This paper focuses on learning complex polyline generalization. First, the requirements for sampled images to ensure the effective learning of complex polyline generalization are analysed. To meet these requirements, new methods for segmenting complex polylines and sampling images are proposed. Second, using the proposed segmentation and sampling method, a use case for the learning of complex polyline generalization using the generative adversarial network model, Pix2Pix, is developed. Third, this use case is applied experimentally for the complex generalization of coastline data from a scale of 1:50,000 to 1:250,000. Additionally, contrast experiments are conducted to compare the proposed segmentation and sampling method with object-based and traditional fixed-size methods. Experimental results show that the images generated using the proposed method are superior to the other two methods in the learning and application of complex polyline generalization. The results generalized for the developed use case are globally reasonable and suitably accurate. Numéro de notice : A2022-651 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1878288 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1080/10106049.2021.1878288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101473 [article]

Recognizing building groups for generalization : a comparative study / Min Deng in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 3 (May 2018) . - pp 187 - 204 Titre : Recognizing building groups for generalization : a comparative study Type de document : Article/Communication Auteurs : Min Deng, Auteur ; Jianbo Tang, Auteur ; Qiliang Liu, Auteur ; Fang Wu, Auteur Année de publication : 2018 Article en page(s) : pp 187 - 204 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] analyse comparative [Termes IGN] Chine [Termes IGN] contrainte géométrique [Termes IGN] généralisation cartographique automatisée [Termes IGN] généralisation du bâti [Vedettes matières IGN] Généralisation Résumé : (Auteur) Recognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approaches, including three approaches that only consider proximity principle and six approaches that consider multiple grouping principles. Real-life dataset at 1:5000, 1:10,000, and 1:50,000 scales provided by National Geomatics Center of China is used to evaluate the performance of these approaches. Buildings at smaller scales are used to construct the benchmarks to test the grouping results at larger scales, and the adjusted Rand index is adopted to indicate the accuracy of the detected groups. Significant tests (Friedman test and Wilcoxon signed-rank test) are also performed to provide both the overall and pairwise comparisons of these approaches. The results show that (1) the average accuracy of most existing approaches is between 0.3 and 0.5, and the performances of these approaches are significantly different; (2) when only proximity is considered, the buffer analysis approach performs significantly better than other approaches; (3) when multiple grouping principles are considered, the local constraint-based approach usually performs better than other approaches; (4) existing approaches that consider similarity and/or continuity seldom improve the performance of building grouping. Numéro de notice : A2018-129 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1302821 Date de publication en ligne : 24/03/2017 En ligne : https://doi.org/10.1080/15230406.2017.1302821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89657 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A typification method for linear pattern in urban building generalisation / Xianyong Gong in Geocarto international, vol 33 n° 2 (February 2018)  

Public [article]  inGeocarto international > vol 33 n° 2 (February 2018) . - pp 189 - 207 Titre : A typification method for linear pattern in urban building generalisation Type de document : Article/Communication Auteurs : Xianyong Gong, Auteur ; Fang Wu, Auteur Année de publication : 2018 Article en page(s) : pp 189 - 207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] généralisation cartographique automatisée [Termes IGN] généralisation du bâti [Termes IGN] itération [Termes IGN] modèle linéaire [Termes IGN] reconnaissance de formes [Termes IGN] triangulation de Delaunay [Termes IGN] typification [Vedettes matières IGN] Généralisation Résumé : (Auteur) This paper presents a typification method for linear pattern in urban building generalization. The proposed method includes two processes. Firstly, structural knowledge in terms of linear pattern is detected using a two-step algorithm taking the advantages of Gestalt visual perception, computational geometry and graph theory. Spatial neighbourhood is captured using interpolated constrained Delaunay triangulation and the resulting proximity graph is pruned to be heterogeneous to get acceptable linear patterns with regard to Gestalt visual perception. Then, a typification strategy is proposed, in which typification is regarded as a progressive and iterative process consisting of elimination, exaggeration and displacement. The typification strategy iteratively executes eliminating the building with minimum overall effect, exaggerating remaining buildings considering key location and spatial characteristics and displacing them to preserve the linear pattern until elimination quantity is satisfied. Experiments show that this proposed strategy is effective and linear patterns are guaranteed with correctness and completeness. Numéro de notice : A2018-034 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1240718 En ligne : https://doi.org/10.1080/10106049.2016.1240718 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89207 [article]