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Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 1905 - 1930 Titre : Estimating urban functional distributions with semantics preserved POI embedding Type de document : Article/Communication Auteurs : Weiming Huang, Auteur ; Lizhen Cui, Auteur ; Meng Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1905 - 1930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] Chine [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] distribution spatiale [Termes IGN] échantillonnage [Termes IGN] lissage de données [Termes IGN] matrice de co-occurrence [Termes IGN] Perceptron multicouche [Termes IGN] point d'intérêt [Termes IGN] triangulation de Delaunay [Termes IGN] zone urbaine Résumé : (auteur) We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions. Numéro de notice : A2022-738 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2040510 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2040510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101714 [article]

Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)  

Public [article]  inComputers & geosciences > vol 158 (January 2022) . - n° 104969 Titre : Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ Type de document : Article/Communication Auteurs : Zhimin Wang, Auteur ; Jiasheng Wang, Auteur ; Kun Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104969 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classe sémantique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image à haute résolution [Termes IGN] image Gaofen [Termes IGN] raisonnement sémantique [Termes IGN] segmentation sémantique Résumé : (auteur) Aiming at solving the problems of inaccurate segmentation of edge targets, inconsistent segmentation of different types of targets, and slow prediction efficiency on semantic segmentation of high-resolution remote sensing images by classical semantic segmentation network, this study proposed a class feature attention mechanism fused with an improved Deeplabv3+ network called CFAMNet for semantic segmentation of common features in remote sensing images. First, the correlation between classes is enhanced using the class feature attention module to extract and process different categories of semantic information better. Second, the multi-parallel atrous spatial pyramid pooling structure is used to enhance the correlation between spaces, to extract the context information of different scales of an image better. Finally, the encoder-decoder structure is used to refine the segmentation results. The segmentation effect of the proposed network is verified by experiments on the public data set GaoFen image dataset (GID). The experimental results show that the CFAMNet can achieve the mean intersection over union (MIOU) and overall accuracy (OA) of 77.22% and 85.01%, respectively, on the GID, thus surpassing the current mainstream semantic segmentation networks. Numéro de notice : A2022-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104969 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99269 [article]

Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 237 - 250 Titre : Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) Type de document : Article/Communication Auteurs : Wenzhi Zhao, Auteur ; Yanchen Bo, Auteur ; Jiage Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 237 - 250 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classe sémantique [Termes IGN] compréhension de l'image [Termes IGN] fusion de données [Termes IGN] image à haute résolution [Termes IGN] reconnaissance d'objets [Termes IGN] scène urbaine Résumé : (Auteur) Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images. Numéro de notice : A2019-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.019 Date de publication en ligne : 29/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92675 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019053	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

IC 2010, Ingénierie des Connaissances 2010, 21es journées francophones, 9 - 10 juin 2010, Nîmes, France / Sylvie Desprès (2010)

Public Titre : IC 2010, Ingénierie des Connaissances 2010, 21es journées francophones, 9 - 10 juin 2010, Nîmes, France : Actes Type de document : Actes de congrès Auteurs : Sylvie Desprès, Éditeur scientifique Editeur : Alès : Ecole des Mines d'Alès Année de publication : 2010 Conférence : IC 2010, 21es journées francophones d'Ingénierie des Connaissances 09/06/2010 11/06/2010 Nimes France OA Proceedings Importance : 320 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-2-911256-25-7 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] classe sémantique [Termes IGN] données médicales [Termes IGN] exploration de données géographiques [Termes IGN] folksonomie [Termes IGN] indexation sémantique [Termes IGN] ingénierie des connaissances [Termes IGN] intelligence artificielle [Termes IGN] ontologie [Termes IGN] recherche d'information [Termes IGN] représentation des connaissances [Termes IGN] web des données [Termes IGN] web sémantique Index. décimale : CG2010 Actes de congrès en 2010 Note de contenu : Conférence invitée 1- Ingénierie des connaissances et textes 2- Méthodologie de conception 3- Données et Web de données 4- Adaptation à l'utilisateur 5- Aspects sociaux et usages 6- Informatique médicale 7- Représentation des connaissances 8- Recherche d'information et indexation 9- Classes sémantiques Numéro de notice : 24617 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Actes DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92187

Voir aussi

• IC 2010, 21es journées francophones d'Ingénierie des Connaissances, 8 - 11 juin 2010, Nîmes, France / Sylvie Desprès (2010)

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
24617-01	CG2010	Livre	Centre de documentation	Congrès	Disponible