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Improving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation / Ahao Wang in GPS solutions, vol 26 n° 2 (April 2022)
[article]
Titre : Improving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation Type de document : Article/Communication Auteurs : Ahao Wang, Auteur ; Yize Zhang, Auteur ; Junping Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] correction ionosphérique
[Termes IGN] modèle ionosphérique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] temps de convergence
[Termes IGN] temps réel
[Termes IGN] teneur verticale totale en électrons
[Termes IGN] transformation polynomialeRésumé : (auteur) The long (re-)convergence time seriously limits many applications of real-time precise point positioning (RTPPP) in challenging environments like urban vehicle navigation and hazards monitoring. Thus, we proposed a real-time fast-positioning model by introducing the regional between-satellite single-differenced (SD) ionospheric constraints into the undifferenced and uncombined PPP (UU-PPP). The line-of-sight ionospheric observables are extracted by the multi-GNSS (GPS + Galileo) UU-PPP method. The polynomial function with simple structure and high efficiency is applied to establish the real-time regional between-satellite SD ionospheric vertical total electron content (VTEC) model. The differential slant total electron content (dSTEC) variations retrieved from three VTEC models are validated with the between-satellite SD and epoch-differenced geometry-free combinations of dual-frequency phase observations. The average RMS values are 0.77, 0.78 and 0.47 TEC unit for the CLK93 real-time VTEC, CODE final GIM and regional between-satellite SD ionospheric VTEC model, respectively. In the positioning domain, the data of ten stations for 12 consecutive days in 2020 were used for implementing kinematic RTPPP with single-frequency (SF) and dual-frequency (DF) observations. Compared with the GPS + Galileo SF-RTPPP based on the GRoup And PHase Ionospheric Correction model, the initialization time of the SD ionospheric-constrained (SDIC) SF-RTPPP when converged to 0.2 m at the 68% confidence level can be improved from 58 to 32 min in horizontal and 72 to 49 min in vertical, and its positioning accuracy can be improved by 29.7 and 20.3% in the horizontal and vertical components, respectively. Meanwhile, the re-convergence errors of SDIC SF-RTPPP from the first epoch can be maintained at 0.15 m in three components. As to GPS + Galileo SDIC DF-RTPPP, the re-convergence time when converged to 0.1 m can be lower than 3 min in horizontal and 9 min in vertical, and the re-convergence errors at the first epoch could even be lower than 0.15 m in horizontal. Hence, the new positioning model can maintain high accuracy and improve the continuity of real-time kinematic positioning in a short time when the number of tracked satellites in the urban or canyon environment was greatly dropped due to signal blocking. Numéro de notice : A2022-107 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01229-z Date de publication en ligne : 21/02/2022 En ligne : https://doi.org/10.1007/s10291-022-01229-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99608
in GPS solutions > vol 26 n° 2 (April 2022) . - n° 39[article]A knowledge representation model based on the geographic spatiotemporal process / Kun Zheng in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : A knowledge representation model based on the geographic spatiotemporal process Type de document : Article/Communication Auteurs : Kun Zheng, Auteur ; Ming Hui Xie, Auteur ; Jin Biao Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 674 - 691 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] ontologie
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] représentation des connaissances
[Termes IGN] représentation du changement
[Termes IGN] représentation géographique
[Termes IGN] réseau sémantiqueRésumé : (auteur) Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. Numéro de notice : A2022-255 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1962527 Date de publication en ligne : 05/08/2021 En ligne : https://doi.org/10.1080/13658816.2021.1962527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100228
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 674 - 691[article]Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
[article]
Titre : Meta-learning based hyperspectral target detection using siamese network Type de document : Article/Communication Auteurs : Yulei Wang, Auteur ; Xi Chen, Auteur ; Fengchao Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5527913 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] classification pixellaire
[Termes IGN] détection de cible
[Termes IGN] espace euclidien
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal siamois
[Termes IGN] tripletRésumé : (auteur) When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance. Numéro de notice : A2022-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3169970 Date de publication en ligne : 22/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3169970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100649
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 5527913[article]MTLM: a multi-task learning model for travel time estimation / Saijun Xu in Geoinformatica, vol 26 n° 2 (April 2022)
[article]
Titre : MTLM: a multi-task learning model for travel time estimation Type de document : Article/Communication Auteurs : Saijun Xu, Auteur ; Ruoqian Zhang, Auteur ; Wanjun Cheng, Auteur ; Jiajie Xu, Auteur Année de publication : 2022 Article en page(s) : pp 379 - 395 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse coût-avantage
[Termes IGN] apprentissage automatique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] durée de trajet
[Termes IGN] modèle de simulation
[Termes IGN] transport collectif
[Termes IGN] transport intermodalRésumé : (auteur) Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods. Numéro de notice : A2022-325 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1007/s10707-020-00422-x Date de publication en ligne : 15/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00422-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100488
in Geoinformatica > vol 26 n° 2 (April 2022) . - pp 379 - 395[article]Multilevel modeling of geographic information systems based on international standards / Suilen H. Alvarado in Software and Systems Modeling, vol 21 n° 2 (April 2022)
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Titre : Multilevel modeling of geographic information systems based on international standards Type de document : Article/Communication Auteurs : Suilen H. Alvarado, Auteur ; Alejandro Cortiñas, Auteur ; Miguel R. Luaces, Auteur ; Oscar Pedreira, Auteur ; Angeles S. Places, Auteur Année de publication : 2022 Article en page(s) : pp 623 - 666 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] architecture orientée modèle
[Termes IGN] métamodèle
[Termes IGN] standard OGC
[Termes IGN] système d'information géographiqueRésumé : (auteur) Even though different applications based on Geographic Information Systems (GIS) provide different features and functions, they all share a set of common concepts (e.g., spatial data types, operations, services), a common architecture, and a common set of technologies. Furthermore, common structures appear repeatedly in different GIS, although they have to be specialized in specific application domains. Multilevel modeling is an approach to model-driven engineering (MDE) in which the number of metamodel levels is not fixed. This approach aims at solving the limitations of a two-level metamodeling approach, which forces the designer to include all the metamodel elements at the same level. In this paper, we address the application of multilevel modeling to the domain of GIS, and we evaluate its potential benefits. Although we do not present a complete set of models, we present four representative scenarios supported by example models. One of them is based on the standards defined by ISO TC/211 and the Open Geospatial Consortium. The other three are based on the EU INSPIRE Directive (territory administration, spatial networks, and facility management). These scenarios show that multilevel modeling can provide more benefits to GIS modeling than a two-level metamodeling approach. Numéro de notice : A2022-330 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10270-021-00901-1 Date de publication en ligne : 02/07/2021 En ligne : https://doi.org/10.1007/s10270-021-00901-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100605
in Software and Systems Modeling > vol 21 n° 2 (April 2022) . - pp 623 - 666[article]PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkPotential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space / Cheikh Mohamedou in Canadian Journal of Forest Research, Vol 52 n° 4 (April 2022)PermalinkRegularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model / Zemin Wu in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkResearch on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)PermalinkSimulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data / Zihao Huang in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkSpatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkSpecies level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkUncertainty estimation for stereo matching based on evidential deep learning / Chen Wang in Pattern recognition, vol 124 (April 2022)PermalinkVD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification / Jihao Li in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkVolunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)PermalinkTravaux actuels d'inventaire des forêts à forte naturalité à l'échelle nationale et européenne / Fabienne Benest in Revue forestière française, vol 73 n° 2 - 3 (2021)PermalinkAboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)PermalinkAccessing spatial knowledge networks with maps / Markus Jobst in International journal of cartography, vol 8 n° 1 (March 2022)PermalinkA l'aide ! Je me suis perdu en zoomant / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkComparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)PermalinkA cost-effective method for reconstructing city-building 3D models from sparse Lidar point clouds / Marek Kulawiak in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkDeep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkDeformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)PermalinkEarly warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkEvaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])PermalinkEvaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)PermalinkÉvaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine / Maxime Mérizette in XYZ, n° 170 (mars 2022)PermalinkExploring the strategy goals and strategy drivers of national mapping, cadastral, and land registry authorities / Erik Hämäläinen in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkExtraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkFlood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)PermalinkHierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification / Yuntao Liu in The Visual Computer, vol 38 n° 3 (March 2022)PermalinkIdentification de relations spatiales par apprentissage profond sur des graphes / Azelle Courtial in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkLand surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkMise à jour du registre de l’EPSG suite aux évolutions du RGF93 / Thierry Gattacceca in XYZ, n° 170 (mars 2022)PermalinkNeural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)PermalinkProbabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkReBankment : un algorithme pour déplacer les talus sur les cartes par moindres carrés / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkRetours d'expérience de la mise en place d'une plateforme collaborative pour le suivi de l'usage du sol / Ana-Maria Olteanu-Raimond in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkRoad network generalization method constrained by residential areas / Zheng Lyu in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkSculpting, cutting, expanding, and contracting the map / Nick Lally in Cartographica, Vol 57 n° 1 (Spring 2022)PermalinkSimulation d'ouragans et de collectes de déchets sur QGIS pour l'amélioration de la collecte des déchets post-ouragan / Quy Thy Truong in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkUnravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkUsing street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)PermalinkVisual vs internal attention mechanisms in deep neural networks for image classification and object detection / Abraham Montoya Obeso in Pattern recognition, vol 123 (March 2022)PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkA method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])Permalink