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Geographic 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)
[article]
Titre : Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis Type de document : Article/Communication Auteurs : Chuan Yin, Auteur ; Binyu Zhang, Auteur ; Wanzeng Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de groupement
[Termes IGN] attribut sémantique
[Termes IGN] granularité (informatique)
[Termes IGN] granularité d'image
[Termes IGN] matrice de co-occurrence
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] relation sémantique
[Termes IGN] réseau sémantique
[Termes IGN] synonymieRésumé : (auteur) Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy. Numéro de notice : A2022-548 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070360 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3390/ijgi11070360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101149
in ISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 360[article]Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)
[article]
Titre : Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis Type de document : Article/Communication Auteurs : Jinpei Chen, Auteur ; Nan Zhi, Auteur ; Haofan Liao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage profond
[Termes IGN] carte ionosphérique mondiale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] correction ionosphérique
[Termes IGN] modèle dynamique
[Termes IGN] positionnement par GNSS
[Termes IGN] temps de convergence
[Termes IGN] teneur verticale totale en électronsRésumé : (auteur) The widely used GNSS correction services for high precision positioning take advantage of accurate real-time TEC forecasting based on vertical total electron content (VTEC) maps. The methods for modeling and forecasting are mainly based on overly simplified assumptions, which in principle cannot reflect the real situations due to limitations of the mathematical formulations. Therefore, these methods cannot comprehensively capture the features of ionospheric TEC in spatial–temporal series. To overcome the problems caused by such assumptions, we combine ConvLSTM (convolutional long short-term memory) with spectrum analysis. The method allows the extraction of high-resolution spatial–temporal patterns of the ionospheric VTEC maps and accelerates the convergence time of neural networks. Extensive experiments have been carried out for short- and long-term forecasting and demonstrated that the performance of our method is better than other state-of-the-art models developed for various time series analysis methods. Based on the data from global ionospheric maps (GIMs) products, the results show that the root-mean-square error (RMSE) of global VTEC forecasting by our method substantially improves for two hours intervals over the years 2015, 2016, 2017 and 2019 compared to existing methods, specifically, 20–50% reduction on 1 or 2 h forecasting in terms of RMSE. In addition, the method is sufficient to support real-time forecasting since it takes less than one second to output global forecasting solutions. With these properties, we can facilitate real-time and highly accurate ionosphere correction services beneficial to numerous GNSS correct services and positioning terminals. Numéro de notice : A2022-378 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01253-z Date de publication en ligne : 13/04/2022 En ligne : https://doi.org/10.1007/s10291-022-01253-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100638
in GPS solutions > vol 26 n° 3 (July 2022) . - n° 69[article]Improving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)
[article]
Titre : Improving remote sensing classification: A deep-learning-assisted model Type de document : Article/Communication Auteurs : Tsimur Davydzenka, Auteur ; Pejman Tahmasebi, Auteur ; Mark Carroll, Auteur Année de publication : 2022 Article en page(s) : n° 105123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image à haute résolution
[Termes IGN] modèle stochastique
[Termes IGN] précision de la classificationRésumé : (auteur) In many industries and applications, obtaining and classifying remote sensing imagery plays a crucial role. The accuracy of classification, in particular the machine learning methods, mainly depends on a multitude of factors, among which one of the most important ones is the amount of training data. Obtaining sufficient amounts of training data, however, can be very difficult or costly, and one must find alternative ways to improve the accuracy of predictions. To this end, a possible solution that we provide in this study is to use a stochastic method for producing variations of the training images that will retain the important class-wide features and thereby enrich the machine learning's “understanding” of the variabilities. As such, we applied a stochastic algorithm to produce additional realizations of the limited input imagery and thereby significantly increase the final overall accuracy in a deep learning method. We found that by enlarging the initial training set by additional realizations, we are able to consistently improve classification accuracy, compared with generic image augmentation approaches. The results of this study show that there is a great opportunity to increase the accuracy of predictions when enough data are not available. Numéro de notice : A2022-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105123 Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100672
in Computers & geosciences > vol 164 (July 2022) . - n° 105123[article]Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)
[article]
Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse visuelle
[Termes IGN] base de données d'images
[Termes IGN] estimation de pose
[Termes IGN] flou
[Termes IGN] localisation basée image
[Termes IGN] localisation basée vision
[Termes IGN] point de repère
[Termes IGN] précision de localisation
[Termes IGN] Ransac (algorithme)
[Termes IGN] réalité de terrain
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070
in International journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836[article]Lidar 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)
[article]
Titre : Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks Type de document : Article/Communication Auteurs : Aurélien Brun, Auteur ; Davide Antonio Cucci, Auteur ; Jan Skaloud, Auteur Année de publication : 2022 Article en page(s) : pp 185 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de points
[Termes IGN] centrale inertielle
[Termes IGN] données lidar
[Termes IGN] filtre de Kalman
[Termes IGN] géoréférencement
[Termes IGN] précision du positionnement
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] signal GNSS
[Termes IGN] superpositionRésumé : (auteur) With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point correspondences between overlapping point clouds and their joint integration (adjustment) together with raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a dynamic network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. The 3D correspondences are formulated as static conditions within the dynamic network and the registered point cloud is generated with significantly higher accuracy based on the corrected trajectory and possibly other parameters determined within the adjustment. We first describe the method for selecting correspondences and how they are inserted into the dynamic network via new observation model while providing an open-source implementation of the solver employed in this work. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors. In the conducted experiments, the method proposed to establish 3D correspondences is effective in determining point–to–point matches across a wide range of geometries such as trees, buildings and cars. Our results demonstrate that the method improves the point cloud registration accuracy (5 in nominal and 10 in emulated GNSS outage conditions within the studied cases), which is otherwise strongly affected by errors in the determined platform attitude or position, and possibly determine unknown boresight angles. The proposed methods remain effective even if only a fraction (0.1%) of the total number of established 3D correspondences are considered in the adjustment. Numéro de notice : A2022-413 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.027 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100764
in ISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 185 - 200[article]Réservation
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