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Extracting building patterns with multilevel graph partition and building grouping / Shihong Du in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 122 (December 2016) . - pp 81 – 96 Titre : Extracting building patterns with multilevel graph partition and building grouping Type de document : Article/Communication Auteurs : Shihong Du, Auteur ; Liqun Luo, Auteur ; Kai Cao, Auteur ; Mi Shu, Auteur Année de publication : 2016 Article en page(s) : pp 81 – 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] extraction automatique [Termes IGN] figure géométrique [Termes IGN] graphe [Termes IGN] partition des données [Termes IGN] paysage urbain [Termes IGN] reconstruction 2D du bâti Résumé : (Auteur) Building patterns are crucial for urban landscape evaluation, social analyses and multiscale spatial data automatic production. Although many studies have been conducted, there is still lack of satisfying results due to the incomplete typology of building patterns and the ineffective extraction methods. This study aims at providing a typology with four types of building patterns (e.g., collinear patterns, curvilinear patterns, parallel and perpendicular groups, and grid patterns) and presenting four integrated strategies for extracting these patterns effectively and efficiently. First, the multilevel graph partition method is utilized to generate globally optimal building clusters considering area, shape and visual distance similarities. In this step, the weights of similarity measurements are automatically estimated using Relief-F algorithm instead of manual selection, thus building clusters with high quality can be obtained. Second, based on the clusters produced in the first step, the extraction strategies group the buildings from each cluster into patterns according to the criteria of proximity, continuity and directionality. The proposed methods are tested using three datasets. The experimental results indicate that the proposed methods can produce satisfying results, and demonstrate that the F-Histogram model is better than the two widely used models (i.e., centroid model and the Voronoi graph) to represent relative directions for building patterns extraction. Numéro de notice : A2016--022 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83885 [article]

Polygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 5 (October 2015) . - pp 716 – 736 Titre : Polygonal clustering analysis using multilevel graph-partition Type de document : Article/Communication Auteurs : Wanyi Wang, Auteur ; Shihong Du, Auteur ; Zhou Guo, Auteur ; Liqun Luo, Auteur Année de publication : 2015 Article en page(s) : pp 716 – 736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] connexité (graphes) [Termes IGN] distance [Termes IGN] données spatiotemporelles [Termes IGN] figure géométrique [Termes IGN] groupe [Termes IGN] partition des données [Termes IGN] polygone [Termes IGN] similitude Résumé : (auteur) Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph-partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. Numéro de notice : A2015-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12124 En ligne : http://dx.doi.org/10.1111/tgis.12124 Format de la ressource électronique : Url artticle Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78325 [article]