Centre dedocumentation
scientifique

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512 Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] codage [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] graphe [Termes descripteurs IGN] mesure géométrique [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] représentation cognitive [Termes descripteurs IGN] représentation spatiale Résumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100 [article]

A vector field model to handle the displacement of multiple conflicts in building generalization / Tinghua Ai in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 8 (August 2015) . - pp 1310 - 1331 Titre : A vector field model to handle the displacement of multiple conflicts in building generalization Type de document : Article/Communication Auteurs : Tinghua Ai, Auteur ; Xiang Zhang, Auteur ; Qi Zhou, Auteur ; Min Yang, Auteur Année de publication : 2015 Article en page(s) : pp 1310 - 1331 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] conflit d'espace [Termes descripteurs IGN] détection de conflit [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] généralisation du bâti [Termes descripteurs IGN] partition des données [Termes descripteurs IGN] relation spatiale [Termes descripteurs IGN] triangulation de Delaunay [Vedettes matières IGN] Généralisation Résumé : (Auteur) In map generalization, the displacement operation attempts to resolve proximity conflicts to guarantee map legibility. Owing to the limited representation space, conflicts may occur between both the same and different features under different contexts. A successful displacement should settle multiple conflicts, suppress the generation of secondary conflicts after moving some objects, and preserve the distribution patterns. The effect of displacement can be understood as a force that pushes related objects away with properties of propagation and distance decay. This study borrows the idea of vector fields from physics discipline and establishes a vector field model to handle the displacement of multiple conflicts in building generalization. A scalar field is first constructed based on a Delaunay triangulation skeleton to partition the buildings being examined (e.g., a street block). Then, we build a vector field to conduct displacement measurements through the detection of conflicts from multiple sources. The direction and magnitude of the displacement force are computed based on an iso-line model of vector field. The experiment shows that this global method can settle multiple conflicts and preserve the spatial relations and important building patterns. Numéro de notice : A2015-604 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1019886 En ligne : https://doi.org/10.1080/13658816.2015.1019886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78025 [article]

Detection and correction of inconsistencies between river networks and contour data by spatial constraint knowledge / Tinghua Ai in Cartography and Geographic Information Science, Vol 42 n° 1 (January 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 1 (January 2015) . - pp 79 - 93 Titre : Detection and correction of inconsistencies between river networks and contour data by spatial constraint knowledge Type de document : Article/Communication Auteurs : Tinghua Ai, Auteur ; Min Yang, Auteur ; Xiang Zhang, Auteur ; Jing Tian, Auteur Année de publication : 2015 Article en page(s) : pp 79 - 93 Note générale : Bibliographie Langues : Anglais (eng) Résumé : (auteur) In the representation of topographic data, the distribution of hydrographic networks should be constrained by the contour model’s landform features. During the integration of topographic databases, however, spatial conflicts may destroy these constraints, generating inconsistencies. This study presents a method to detect and correct inconsistencies between river networks and contour data by spatial knowledge. First, structured terrain features are extracted from the contour-based geometric representation and matching relationships between rivers and contours are constructed based on spatial knowledge of the distribution of rivers and talwegs. We then propose a distance metric for measuring differences and identifying inconsistencies between the matched river and contour features. Three correction approaches are provided for different inconsistency situations, including river adjustment referenced to the contour, contour adjustment referenced to the river and adjustment of both river and contour to middle positions. We apply the proposed method to the integration and maintenance of national topographic infrastructure in order to demonstrate its effectiveness. Numéro de notice : A2015-235 Affiliation des auteurs : non IGN Nature : Article DOI : 10.1080/15230406.2014.956673 En ligne : https://doi.org/10.1080/15230406.2014.956673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76228 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2015011	SL	Revue	Centre de documentation	Revues en salle	Disponible