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Auteur Wenhao Yu |
Documents disponibles écrits par cet auteur (6)
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An integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
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Titre : An integrated method for DEM simplification with terrain structural features and smooth morphology preserved Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Yifan Zhang, Auteur ; Tinghua Ai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 273 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse structurelle
[Termes IGN] arête
[Termes IGN] carte géomorphologique
[Termes IGN] filtrage statistique
[Termes IGN] ligne caractéristique
[Termes IGN] limite de terrain
[Termes IGN] modèle numérique de surface
[Termes IGN] visualisation multiéchelleRésumé : (auteur) As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations. Numéro de notice : A2021-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1772479 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1772479 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96747
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 273 - 295[article]A heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])
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Titre : A heuristic approach to the generalization of complex building groups in urban villages Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Qi Zhou, Auteur ; Rong Zhao, Auteur Année de publication : 2021 Article en page(s) : pp 155 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] empreinte
[Termes IGN] généralisation du bâti
[Termes IGN] méthode heuristique
[Termes IGN] représentation multiple
[Termes IGN] triangulation de Delaunay
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The generalization of building footprints acts as the basis of multi-scale mapping. Most of the previous studies focus on the generalization of regular building clusters within a wide neighbourhood, but only few has concerned about the generalization of cluttered building clusters within the narrow space such as urban village. The buildings in urban villages show special characteristics in terms of individual properties and group properties, and thus their map generalization processes are often limited. This study proposes a framework to generalize the cluttered building clusters that allows for multi-scale mapping. It first adopts a heuristic method to group adjacent buildings based on the Delaunay triangulation model and then aggregates and simplifies each building group separately. Given that the aggregated buildings in urban villages often show cluttered alignments, our method further trims the jagged boundaries of building footprints by extracting the gap space between neighbouring buildings from the Delaunay triangulation model. Numéro de notice : A2021-084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.159046 Date de publication en ligne : 25/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1590463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96843
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 155 - 179[article]Recognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
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Titre : Recognition of building group patterns using graph convolutional network Type de document : Article/Communication Auteurs : Rong Zhao, Auteur ; Tinghua Ai, Auteur ; Wenhao Yu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 400 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données topographiques
[Termes IGN] espace urbain
[Termes IGN] généralisation du bâti
[Termes IGN] graphe
[Termes IGN] modélisation du bâti
[Termes IGN] reconnaissance de formesRésumé : (auteur) Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods. Numéro de notice : A2020-510 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1757512 Date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1080/15230406.2020.1757512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95663
in Cartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 400 - 417[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Quality assessment in point feature generalization with pattern preserved / Wenhao Yu in Transactions in GIS, vol 22 n° 3 (June 2018)
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Titre : Quality assessment in point feature generalization with pattern preserved Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur Année de publication : 2018 Article en page(s) : pp 872 - 888 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse texturale
[Termes IGN] objet géographique ponctuel
[Termes IGN] qualité du processus
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Geographical features often show certain spatial patterns on a map in terms of the arrangement of point symbols. These patterns are essentially related to the underlying geographical processes and landscapes. Thus, when deriving small‐scale maps from a large‐scale map, one of the most important constraints that cartographers or systems should follow is to retain the basic patterns of point objects on the target map. However, no research in the literature currently evaluates the quality of point feature generalization in terms of spatial pattern. This study proposes an approach to quantitatively measure the pattern change after generalization. The basic idea of the approach is to extend advanced image analysis techniques (e.g., texture recognition) to measure the patterns of point objects in a map space. Specifically, there are two main steps: firstly, the original space is converted into the raster space by utilizing a regularly spaced grid (i.e., a grayscale image) with cell attributes representing the local intensity level of point features; secondly, the texture analysis operation is performed on the grid to obtain the feature descriptors of the point pattern. The experimental results demonstrate that the proposed approach is effective in comparing the point patterns before and after generalization. Numéro de notice : A2018-581 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12339 Date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1111/tgis.12339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92329
in Transactions in GIS > vol 22 n° 3 (June 2018) . - pp 872 - 888[article]The analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
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Titre : The analysis and measurement of building patterns using texton co-occurrence matrices Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Tinghua Ai, Auteur ; Pengcheng Liu, Auteur ; Xiaoqiang Cheng, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] données vectorielles
[Termes IGN] matrice de co-occurrence
[Termes IGN] métrique
[Termes IGN] modèle géométrique du bâti
[Termes IGN] reconnaissance de formes
[Termes IGN] reconstruction 2D du bâti
[Termes IGN] tessellation
[Termes IGN] triangulation de Delaunay
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) The representation and analysis of building patterns are critical for characterizing urban scenes and making decisions in urban planning. The evaluation of building patterns is a difficult spatial analysis problem that exhibits properties of symbolization, homogeneity and regularity. Open issues in this field include the development of approaches for representing building patterns and vector-based methods for computing various pattern metrics. In the image analysis domain, there are many methods for pattern recognition (e.g., texture analysis), but there are few corresponding solutions for vector data. The aim of this research is to develop several building pattern metrics and offer a texton co-occurrence matrix (TCM)-based method to quantitatively evaluate the features of building patterns. The procedure first constructs a spatial field based on a Delaunay triangulation skeleton to partition a set of buildings into a set of tessellation cells. The tessellations of building clusters have a similar structure as image representations, in that each cell corresponds to an image pixel. We then use the texton analysis to establish a matrix to describe the tessellation structure, including the neighboring relationships and individual attribute information. Finally, a set of feature descriptors is obtained from the TCM to capture the texture-related information of building groups. Through experiments on building pattern analysis and spatial queries, we show that the results of TCM-based evaluation of building patterns are consistent with those of human cognition. Numéro de notice : A2017-242 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1265121 En ligne : http://dx.doi.org/10.1080/13658816.2016.1265121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85178
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017)[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)Permalink