Descripteur
Documents disponibles dans cette catégorie (5291)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Deep learning method for Chinese multisource point of interest matching / Pengpeng Li in Computers, Environment and Urban Systems, vol 96 (September 2022)
[article]
Titre : Deep learning method for Chinese multisource point of interest matching Type de document : Article/Communication Auteurs : Pengpeng Li, Auteur ; Jiping Liu, Auteur ; An Luo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage profond
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] inférence sémantique
[Termes IGN] information sémantique
[Termes IGN] point d'intérêt
[Termes IGN] représentation vectorielle
[Termes IGN] traitement du langage naturelRésumé : (auteur) Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods. Numéro de notice : A2022-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101821 Date de publication en ligne : 18/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101053
in Computers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101821[article]Pourquoi le rendu des zones rocheuses sur les nouvelles cartes IGN est-il si différent de l’ancien ? / Paul Courbon in XYZ, n° 172 (septembre 2022)
[article]
Titre : Pourquoi le rendu des zones rocheuses sur les nouvelles cartes IGN est-il si différent de l’ancien ? Type de document : Article/Communication Auteurs : Paul Courbon, Auteur ; Dominique Foubert, Auteur Année de publication : 2022 Article en page(s) : pp 27 - 30 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Rédaction cartographique
[Termes IGN] BD Topo
[Termes IGN] carte Top 25
[Termes IGN] carte topographique
[Termes IGN] courbe de niveau
[Termes IGN] falaise
[Termes IGN] géoportail
[Termes IGN] représentation cartographique
[Termes IGN] rocher
[Termes IGN] visualisation cartographiqueRésumé : (Auteur) La rédaction de XYZ est alertée par Paul Courbon, l'un de nos auteurs, sur le rendu des zones rocheuses sur les nouvelles cartes IGN. Il compare les anciennes cartes au 1:25000 et les nouvelles, que l’on peut également visualiser sur le Géoportail. Excès d’automatisation du dessin, mauvais choix dans les méthodes de restitution, économie des moyens en personnels, Paul essaie de comprendre pourquoi il ne retrouve plus les falaises d’antan, leurs courbes de niveau et leurs ombrages. Sollicité, Dominique Foubert, ancien topographe du service de la cartographie à l’IGN, nous déroule des pistes de réflexion liées à l’historique de la production des cartes. Effectivement, grâce à l’appui de Christophe Pécot, les souvenirs se précisent et Dominique nous explique “pourquoi et comment” on en est arrivé là. Numéro de notice : A2022-673 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Date de publication en ligne : 01/09/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101637
in XYZ > n° 172 (septembre 2022) . - pp 27 - 30[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2022031 RAB Revue Centre de documentation En réserve L003 Disponible Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)
[article]
Titre : Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices Type de document : Article/Communication Auteurs : Najmeh Mozaffaree Pour, Auteur ; Oleksandr Karasov, Auteur ; Iuliia Burdun, Auteur ; Tõnu Oja, Auteur Année de publication : 2022 Article en page(s) : n° 584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] croissance urbaine
[Termes IGN] Estonie
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-8
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000–2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km2 in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity. Numéro de notice : A2022-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10661-022-10266-7 Date de publication en ligne : 13/07/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10266-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101258
in Environmental Monitoring and Assessment > vol 194 n° 9 (September 2022) . - n° 584[article]Effective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol 81 n° 20 (August 2022)
[article]
Titre : Effective CBIR based on hybrid image features and multilevel approach Type de document : Article/Communication Auteurs : D. Latha, Auteur ; A. Geetha, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'images
[Termes IGN] écart type
[Termes IGN] espace colorimétrique
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] matrice de co-occurrence
[Termes IGN] motif binaire local
[Termes IGN] niveau de gris (image)
[Termes IGN] observation multiniveaux
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] saturation de la couleur
[Termes IGN] texture d'image
[Termes IGN] transformation intensité-teinte-saturationRésumé : (auteur) Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR based on hybrid image features and multilevel approach (CBIR_LTP_GLCM) integrates the hybrid features such as color features and texture features, along with multilevel approach. The color features such as mean and standard deviation are adopted in the proposed method to represent the global color properties of an image. This method manipulates the color input-image by processing the Hue, Saturation and Value channels of the HSV color space. This novel work is enriched with the image feature derived from Local Ternary Pattern (LTP) in addition with GLCM. So, the proposed method CBIR_LTP_GLCM is potentially charged with meaningful modifications travelling with color image manipulation and extended image retrieval accuracy with the aid of multilevel approach. The proposed methodology is experimentally compared with the existing recent CBIR versions by using the standard database such as Corel-1 k, and a user contributed database named DB_VEG. Numéro de notice : A2022-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11042-022-12588-7 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1007/s11042-022-12588-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100337
in Multimedia tools and applications > vol 81 n° 20 (August 2022) . - pp[article]Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images / Zhiyong Lv in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
Titre : Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images Type de document : Article/Communication Auteurs : Zhiyong Lv, Auteur ; Fengjun Wang, Auteur ; Guoqing Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4412712 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] détection de changement
[Termes IGN] image Landsat-TM
[Termes IGN] jeu de données
[Termes IGN] occupation du sol
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Land cover change detection (LCCD) using remote sensing images (RSIs) plays an important role in natural disaster evaluation, forest deformation monitoring, and wildfire destruction detection. However, bitemporal images are usually acquired at different atmospheric conditions, such as sun height and soil moisture, which usually cause pseudo and noise change in the change detection map. Changed areas on the ground also generally have various shapes and sizes, consequently making the utilization of spatial contextual information a challenging task. In this article, we design a novel neural network with a spatial–spectral attention mechanism and multiscale dilation convolution modules. This work is based on the previously demonstrated promising performance of convolutional neural network for LCCD with RSIs and attempts to capture more positive changes and further enhance the detection accuracies. The learning of the proposed neural network is guided with a change magnitude image. The performance and feasibility of the proposed network are validated with four pairs of RSIs that depict real land cover change events on the Earth’s surface. Comparison of the performance of the proposed approach with that of five state-of-art methods indicates the superiority of the proposed network in terms of ten quantitative evaluation metrics and visual performance. Such as, the proposed network achieved an improvement of about 0.08%–14.87% in terms of overall accuracy (OA) for Dataset-A. Numéro de notice : A2022-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3197901 Date de publication en ligne : 17/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3197901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101516
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 4412712[article]3D-GIS parametric modelling for virtual urban simulation using CityEngine / Ibrahim M. Badwi in Annals of GIS, vol 28 n° 3 (July 2022)PermalinkCartographie : Le dispositif national de suivi des bocages / Sophie Morin Pinaud in Courrier de la nature, No special 2022 ([01/07/2022])PermalinkGeographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)PermalinkHeat wave-induced augmentation of surface urban heat islands strongly regulated by rural background / Shiqi Miao in Sustainable Cities and Society, vol 82 (July 2022)PermalinkIntegration of GNSS observations with volunteered geographic information for improved navigation performance / Tarek Hassan in Journal of applied geodesy, vol 16 n° 3 (July 2022)PermalinkInvestigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)PermalinkLidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkLittoraux sous double surveillance / Laurent Polidori in Géomètre, n° 2204 (juillet-août 2022)PermalinkA participatory trail web map based on open source technologies / Joshua Gore in International journal of cartography, vol 8 n° 2 (July 2022)PermalinkPolyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)Permalink