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Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)
[article]
Titre : Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands Type de document : Article/Communication Auteurs : Katrin Krzepek, Auteur ; Jacob Schmidt, Auteur ; Dorota Iwaszczuk, Auteur Année de publication : 2022 Article en page(s) : pp 561 - 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage non-dirigé
[Termes IGN] aquifère
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] bande C
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] puits de carbone
[Termes IGN] seuillage d'image
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] tourbièreRésumé : (auteur) Peatlands as natural carbon sinks have a major impact on the climate balance and should therefore be monitored and protected. The hydrology of the peatland serves as an indicator of the carbon storage capacity. Hence, we investigate the question how suitable different remote sensing data are for monitoring the size of open water surface and the water table depth (WTD) of a peatland ecosystem. Furthermore, we examine the potential of combining remote sensing data for this purpose. We use C-band synthetic aperture radar (SAR) data from Sentinel-1 and multi-spectral data from Sentinel-2. The radar backscatter σ0, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are calculated and used for consideration of the WTD and the lake size. For the measurement of the lake size, we implement and investigate the methods: random forest, adaptive thresholding and an analysis according to the Dempster–Shafer theory. Correlations between WTD and the remote sensing data σ0 as well as NDWI are investigated. When looking at the individual data sets the results of our case study show that the VH polarized σ0 data produces the clearest delineation of the peatland lake. However the adaptive thresholding of the weighted fusion image of σ0-VH, σ0-VV and MNDWI, and the random forest algorithm with all three data sets as input proves to be the most suitable for determining the lake area. The correlation coefficients between σ0/NDWI and WTD vary greatly and lie in ranges of low to moderate correlation. Numéro de notice : A2022-942 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41064-022-00216-w Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.1007/s41064-022-00216-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102876
in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science > vol 90 n° 6 (December 2022) . - pp 561 - 575[article]Galileo High Accuracy Service (HAS) ou le service de haute précision de Galileo / Bernard Flacelière in XYZ, n° 173 (décembre 2022)
[article]
Titre : Galileo High Accuracy Service (HAS) ou le service de haute précision de Galileo Type de document : Article/Communication Auteurs : Bernard Flacelière, Auteur Année de publication : 2022 Article en page(s) : pp 28 - 29 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] constellation Galileo
[Termes IGN] correction
[Termes IGN] erreur de positionnement
[Termes IGN] positionnement ponctuel précis
[Termes IGN] précision décimétrique
[Termes IGN] récepteur Galileo
[Termes IGN] temps réelRésumé : (Editeur) Deux fois par an, au printemps et en automne, les réunions, actuellement en mode hybride, du CNIG (Conseil national de l’information géolocalisée) réunissent les professionnels. La dernière réunion du groupe de travail G&P (GNSS et positionnement) a eu lieu le 13 octobre 2022 à l’ENSG tandis que la réunion plénière de la commission GéoPos (Géopositionnement) s’est tenue le 14 octobre à l’IGN. Lors de la réunion du GT G&P, durant l’après?midi thématique, Ignacio Fernández-Hernández de la Commission européenne nous a présenté les aspects actuels et futurs du service de haute précision de Galileo (Current and future aspects of Galileo HAS). Il est résumé ici les faits marquants de cet exposé. Bientôt, vous pourrez vous positionner en temps réel avec une précision décimétrique en utilisant la constellation Galileo et un récepteur compatible. Numéro de notice : A2022-913 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102275
in XYZ > n° 173 (décembre 2022) . - pp 28 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2022041 RAB Revue Centre de documentation En réserve L003 Disponible Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)
[article]
Titre : Geographic named entity recognition by employing natural language processing and an improved BERT model Type de document : Article/Communication Auteurs : Liufeng Tao, Auteur ; Zhong Xie, Auteur ; Dexin Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Chine
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données publiques
[Termes IGN] jeu de données
[Termes IGN] reconnaissance de caractères
[Termes IGN] reconnaissance de noms
[Termes IGN] test de performance
[Termes IGN] toponyme
[Termes IGN] traitement du langage naturelRésumé : (auteur) Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models. Numéro de notice : A2022-945 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120598 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.3390/ijgi11120598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102178
in ISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 598[article]Georeferencing accuracy assessment of historical aerial photos using a custom-built online georeferencing tool / Su Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)
[article]
Titre : Georeferencing accuracy assessment of historical aerial photos using a custom-built online georeferencing tool Type de document : Article/Communication Auteurs : Su Zhang, Auteur ; Hays A. Barrett, Auteur ; Shirley V. Baros, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 582 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] application web
[Termes IGN] ArcGIS
[Termes IGN] ArcMap
[Termes IGN] géoréférencement indirect
[Termes IGN] image ancienne
[Termes IGN] photographie aérienne
[Termes IGN] point d'appui
[Termes IGN] précision du positionnement
[Termes IGN] standard OGCRésumé : (auteur) As one of the earliest forms of remote sensing, aerial photography has been regarded as an important part of the mapmaking process. Aerial photos, especially historical aerial photos, provide significant amount of valuable information for many applications and fields. However, due to limited funding support, most historical aerial photos have not been digitized and georeferenced yet, which substantially limits their utility for today’s computer-based image processing and analysis. Traditionally, historical aerial photos are georeferenced with desktop GIS software applications. However, this method is expensive, time-consuming, and labor-intensive. To address these limitations, this research developed a custom-built online georeferencing tool to enable georeferencing digitized historical aerial photos in a web environment, which is able to georeference historical aerial photos in a rapid and cost-effective manner. To evaluate the georeferencing performance, a set of 50 historical aerial photos were georeferenced with not only the developed online georeferencing tool but also two commercial desktop software programs. Research results revealed the custom-built online georeferencing tool provided the highest degree of accuracy while maximizing its accessibility. Numéro de notice : A2022-860 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi11120582 Date de publication en ligne : 22/11/2022 En ligne : https://doi.org/10.3390/ijgi11120582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102177
in ISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 582[article]Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] ArcGIS
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] classification par arbre de décision
[Termes IGN] colinéarité
[Termes IGN] estimation bayesienne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] TéhéranRésumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666
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Kaloop in Survey review, vol 54 n° 387 (November 2022)PermalinkImproving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)PermalinkA joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkA machine learning approach for detecting rescue requests from social media / Zheye Wang in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)PermalinkMachine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkMeasuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)PermalinkPoint2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds / Li Li in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkModelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkAn efficient method to compensate receiver clock jumps in real-time precise point positioning / Shaoguang Xu in Remote sensing, vol 14 n° 20 (October-2 2022)PermalinkComparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkFlash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkGIS and MCDMA prioritization based modeling for sub-watershed in Bastora river basin / Raid Mahmood Faisal in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkModelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkApplication of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkAugmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)PermalinkChallenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images : A systematic review / Sahar S. 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Martins in Remote sensing of environment, vol 280 (October 2022)PermalinkDSNUNet: An improved forest change detection network by combining Sentinel-1 and Sentinel-2 images / Jiawei Jiang in Remote sensing, vol 14 n° 19 (October-1 2022)PermalinkEstimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkEvaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkHabitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France / Christophe Sauser in Biological Conservation, vol 274 (octobre 2022)PermalinkIdentify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkIdentifying the key resources and missing elements to build a knowledge graph dedicated to spatial dataset search / Mehdi Zrhal in Procedia Computer Science, vol 207 (2022)PermalinkIncremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)PermalinkInvestigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)PermalinkMachine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)PermalinkMachine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)PermalinkNovel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)PermalinkPPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna / Xingxing Li in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkPrecise onboard time synchronization for LEO satellites / Florian Kunzi in Navigation : journal of the Institute of navigation, vol 69 n° 3 (Fall 2022)PermalinkPyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. 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