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Applications and challenges of GRACE and GRACE follow-on satellite gravimetry / Jianli Chen in Surveys in Geophysics, vol 43 n° 1 (February 2022)
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Titre : Applications and challenges of GRACE and GRACE follow-on satellite gravimetry Type de document : Article/Communication Auteurs : Jianli Chen, Auteur ; Anny Cazenave, Auteur ; Christoph Dahle, Auteur ; William Llovel, Auteur ; Isabelle Panet , Auteur ; Julia Pfeffer, Auteur ; Lorena Moreira, Auteur
Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 305 - 345 Note générale : bibliographie
This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (GRACEFUL Synergy Grant agreement No 855677).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse diachronique
[Termes IGN] champ de gravitation
[Termes IGN] champ de pesanteur terrestre
[Termes IGN] changement climatique
[Termes IGN] cryosphère
[Termes IGN] détection de changement
[Termes IGN] données GRACE
[Termes IGN] gravimétrie spatiale
[Termes IGN] hydrosphère
[Termes IGN] masse
[Termes IGN] niveau de la merRésumé : (auteur) Time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions have opened up a new avenue of opportunities for studying large-scale mass redistribution and transport in the Earth system. Over the past 19 years, GRACE/GRACE-FO time-variable gravity measurements have been widely used to study mass variations in diferent components of the Earth system, including the hydrosphere, ocean, cryosphere, and solid Earth, and signifcantly improved our understanding of long-term variability of the climate system. We carry out a comprehensive review of GRACE/GRACE-FO satellite gravimetry, time-variable gravity felds, data processing methods, and major applications in several diferent felds, includingterrestrial water storage change, global ocean mass variation, ice sheets and glaciers mass balance, and deformation of the solid Earth. We discuss in detail several major challenges we need to face when using GRACE/GRACE-FO time-variable gravity measurements to study mass changes, and how we should address them. We also discuss the potential of satellite gravimetry in detecting gravitational changes that are believed to originate from the deep Earth. The extended record of GRACE/GRACE-FO gravity series, with expected continuous improvements in the coming years, will lead to a broader range of applications and improve our understanding of both climate change and the Earth system. Numéro de notice : A2022-113 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10712-021-09685-x Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1007/s10712-021-09685-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99631
in Surveys in Geophysics > vol 43 n° 1 (February 2022) . - pp 305 - 345[article]Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])
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Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Birmanie
[Termes IGN] détection du bâti
[Termes IGN] empreinte
[Termes IGN] image Geoeye
[Termes IGN] image isolée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] vision monoculaireRésumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 792 - 812[article]A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
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Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113[article]Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)
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Titre : Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation Type de document : Article/Communication Auteurs : Ramazan Unlu, Auteur ; Recep Kiriş, Auteur Année de publication : 2022 Article en page(s) : pp 685 - 694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] bâtiment
[Termes IGN] classification par nuées dynamiques
[Termes IGN] détection de changement
[Termes IGN] dommage matériel
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation d'image
[Termes IGN] séismeRésumé : (auteur) Detecting damaged buildings after an earthquake as quickly as possible is important for emergency teams to reach these buildings and save the lives of many people. Today, damaged buildings after the earthquake are carried out by the survivors contacting the authorities or using some air vehicles such as helicopters. In this study, AI-based systems were tested to detect damaged or destroyed buildings by integrating into street camera systems after unexpected disasters. For this purpose, we have used VGG-16, VGG-19, and NASNet convolutional neural network models which are often used for image recognition problems in the literature to detect damaged buildings. In order to effectively implement these models, we have first segmented all the images with the K-means clustering algorithm. Thereafter, for the first phase of this study, segmented images labeled “damaged buildings” and “normal” were classified and the VGG-19 model was the most successful model with a 90% accuracy in the test set. Besides, as the second phase of the study, we have created a multiclass classification problem by labeling segmented images as “damaged buildings,” “less damaged buildings,” and “normal.” The same three architectures are used to achieve the most accurate classification results on the test set. VGG-19 and VGG-16, and NASNet have achieved considerable success in the test set with about 70%, 67%, and 62% accuracy, respectively. Numéro de notice : A2022-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02043-9 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02043-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100039
in The Visual Computer > vol 38 n° 2 (February 2022) . - pp 685 - 694[article]Five decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms / K.J. Kirby in Forest ecology and management, vol 505 (February-1 2022)
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Titre : Five decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms Type de document : Article/Communication Auteurs : K.J. Kirby, Auteur ; D.R. Bazely, Auteur ; E.A. Goldberg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119896 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] biomasse forestière
[Termes IGN] Brachypodium (genre)
[Termes IGN] Cervidae
[Termes IGN] composition floristique
[Termes IGN] dépérissement
[Termes IGN] détection de changement
[Termes IGN] eutrophisation
[Termes IGN] flore forestière
[Termes IGN] forêt tempérée
[Termes IGN] Fraxinus excelsior
[Termes IGN] gestion forestière
[Termes IGN] maladie phytosanitaire
[Termes IGN] richesse floristique
[Termes IGN] Royaume-Uni
[Termes IGN] Tracheophyta
[Vedettes matières IGN] ForesterieRésumé : (auteur) We explore how the ground flora of a temperate woodland (Wytham Woods, southern England) changed in terms of species-richness, cover and biomass over five decades; what the drivers of change were; and possible future change as a consequence of the decline in Fraxinus excelsior as a canopy dominant. Vascular plants were recorded from 164 permanent, 10x10 m plots, distributed as a 141 m grid, in 1974, 1991, 1999, 2012, and 2018. Species presence and frequency/abundance in each plot were estimated and used to model biomass changes. Changes in species-richness, vegetation composition and structure were analysed. Stands opened out by thinning or which became denser through tree growth gained or lost species respectively, particularly non-woodland species. Deer pressure favoured the spread of Brachypodium sylvaticum and reduced Rubus fruticosus. No obvious impacts of climate change, eutrophication or of invasive species were detected in the plot records although other signs suggest these are starting to affect the flora. Just 12 out of 235 species contributed 47% of all species occurrences, 82% of the vegetation cover and 87% of the modelled biomass. We conclude that the ground flora is highly variable over decadal timescales, but the patterns of change observed differ according to the measures used (species richness, cover, biomass, etc). Site level drivers in the short-term swamped effects of slower acting regional/global drivers. Legacy effects were seen in the greater richness of specialists in the older woodland. While some impacts can be mitigated by management, others are largely beyond control at the site level. Numéro de notice : A2022-041 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.foreco.2021.119896 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119896 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99389
in Forest ecology and management > vol 505 (February-1 2022) . - n° 119896[article]Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
PermalinkMapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)
PermalinkMapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)
PermalinkMulti-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)
PermalinkNovel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)
PermalinkObject recognition algorithm based on optimized nonlinear activation function-global convolutional neural network / Feng-Ping An in The Visual Computer, vol 38 n° 2 (February 2022)
PermalinkPCEDNet: a lightweight neural network for fast and interactive edge detection in 3D point clouds / Chems-Eddine Himeur in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)
PermalinkSiamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)
PermalinkSynergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])
PermalinkThree-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
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