Descripteur
Documents disponibles dans cette catégorie (2500)
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
3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)
[article]
Titre : 3D modeling method for dome structure using digital geological map and DEM Type de document : Article/Communication Auteurs : Xian-Yu Liu, Auteur ; An-Bo Li, Auteur ; Hao Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 339 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte géologique
[Termes IGN] carte stratigraphique
[Termes IGN] courbe de Bézier
[Termes IGN] modèle géologique
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] structure géologiqueRésumé : (auteur) Geological maps have wide coverage with low acquisition difficulty. When other geological survey data are scarce, they are a valuable source of geological structure information for geological modeling. However, for structures with large deformation, geological map information has difficulty meeting the requirement of its 3D geological modeling. Therefore, this paper takes the dome structure as an example to explore a 3D modeling method based on geological maps, DEM and related geological knowledge. The method includes: (1) adaptively calculating the attitude of points on the stratigraphic boundaries; (2) inferring and generating the bottom boundary of the model from the attitude data of the boundary points; (3) generating the model interface constrained by Bézier curves based on the bottom boundary; (4) generating the top and bottom surfaces of the stratum; and (5) stitching each surface of the geological body to generate the final dome model. Case studies of the dome in Wulongshan in China and the Richat structure in Mauritania show that this method can build a solid model of the dome based only on geological maps and DEM data, whose morphological features are basically consistent with those embodied in the section view or the model generated by traditional methods. Numéro de notice : A2022-482 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11060339 Date de publication en ligne : 07/06/2022 En ligne : https://doi.org/10.3390/ijgi11060339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100895
in ISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 339[article]Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])
[article]
Titre : Assessing and mapping landslide susceptibility using different machine learning methods Type de document : Article/Communication Auteurs : Osman Orhan, Auteur ; Suleyman Sefa Bilgilioglu, Auteur ; Zehra Kaya, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2795 - 2820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] effondrement de terrain
[Termes IGN] lithologie
[Termes IGN] pente
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] TurquieRésumé : (auteur) The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. Numéro de notice : A2022-594 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837258 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101298
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2795 - 2820[article]On the consistency of coastal sea-level measurements in the Mediterranean Sea from tide gauges and satellite radar altimetry / Sara Bruni in Journal of geodesy, vol 96 n° 6 (June 2022)
[article]
Titre : On the consistency of coastal sea-level measurements in the Mediterranean Sea from tide gauges and satellite radar altimetry Type de document : Article/Communication Auteurs : Sara Bruni, Auteur ; Luciana Fenoglio, Auteur ; Fabio Raicich, Auteur ; Susanna Zerbini, Auteur Année de publication : 2022 Article en page(s) : n° 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse comparative
[Termes IGN] cohérence des données
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] données altimétriques
[Termes IGN] données marégraphiques
[Termes IGN] Méditerranée, mer
[Termes IGN] niveau de la merRésumé : (auteur) We assess the consistency of sea-level variability derived from tide-gauge (TG) and satellite radar altimeter (ALT) data acquired along the coasts of the Mediterranean Sea. For a coherent comparison between these techniques, we use GNSS observations to characterize the local vertical land movement embedded in TG records, but not affecting ALT data. We first investigate the performance of CMEMS, a gridded altimeter product covering the period 1993–2019. TG and GNSS series are not required to cover the whole altimeter period. The inter-technique comparison reveals good agreement at annual and semi-annual scales, but also the occasional occurrence of nonlinear discrepancies impacting trend estimation. Large-scale patterns of variability are observed in the Ionian region and along the continental shores from the Alboran to the Adriatic Sea. The estimates of linear trends based on TG + GNSS or CMEMS observations are found consistent within 1σ at 27/45 sites, with the best agreement in the Western Mediterranean and Adriatic Seas. We also consider the X-TRACK/ALES altimeter dataset, provided along the tracks of the Jason missions (2002–2018) and optimized for coastal applications. In this case, we identify only 12 sites suitable for the comparison. The results show that inter-technique consistency is impacted by the length of the series used in the comparison. Optimum agreement between X-TRACK/ALES and TG + GNSS trends is reached at the two sites closer to a satellite track. However, we find sites where X-TRACK/ALES-derived sea-level trends present suspicious along-track variations at Numéro de notice : A2022-452 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01626-9 En ligne : http://dx.doi.org/10.1007/s00190-022-01626-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100976
in Journal of geodesy > vol 96 n° 6 (June 2022) . - n° 41[article]Cliff change detection using siamese KPCONV deep network on 3D point clouds / Iris de Gelis in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)
[article]
Titre : Cliff change detection using siamese KPCONV deep network on 3D point clouds Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Zoé Bessin, Auteur ; Pauline Letortu, Auteur ; Marion Jaud, Auteur ; C. Delacourt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 649 - 656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] érosion côtière
[Termes IGN] falaise
[Termes IGN] semis de points
[Termes IGN] surveillance géologiqueMots-clés libres : KPConv = Kernel Point Convolution Résumé : (auteur) Mainly depending on their lithology, coastal cliffs are prone to changes due to erosion. This erosion could increase due to climate change leading to potential threats for coastal users, assets, or infrastructure. Thus, it is important to be able to understand and characterize cliff face changes at fine scale. Usually, monitoring is conducted thanks to distance computation and manual analysis of each cliff face over 3D point clouds to be able to study 3D dynamics of cliffs. This is time consuming and inclined to each one judgment in particular when dealing with 3D point clouds data. Indeed, 3D point clouds characteristics (sparsity, impossibility of working on a classical top view representation, volume of data, …) make their processing harder than 2D images. Last decades, an increase of performance of machine learning methods for earth observation purposes has been performed. To the best of our knowledge, deep learning has never been used for 3D change detection and categorization in coastal cliffs. Lately, Siamese KPConv brings successful results for change detection and categorization into 3D point clouds in urban area. Although the case study is different by its more random characteristics and its complex geometry, we demonstrate here that this method also allows to extract and categorize changes on coastal cliff face. Results over the study area of Petit Ailly cliffs in Varengeville-sur-Mer (France) are very promising qualitatively as well as quantitatively: erosion is retrieved with an intersection over union score of 83.86 %. Numéro de notice : A2022-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2022-649-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-649-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100779
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 649 - 656[article]Efficient dike monitoring using terrestrial SFM photogrammetry / Laurent Froideval in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Efficient dike monitoring using terrestrial SFM photogrammetry Type de document : Article/Communication Auteurs : Laurent Froideval, Auteur ; Christophe Conessa, Auteur ; Xavier Pellerin Le Bas, Auteur ; Laurent Benoit, Auteur ; Dominique Mouazé, Auteur Année de publication : 2022 Article en page(s) : pp 359 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] digue
[Termes IGN] sable
[Termes IGN] semis de points
[Termes IGN] série temporelle
[Termes IGN] structure-from-motion
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) Nature based solutions are growing rapidly in order to mitigate in the near future the effects of climate change and rise of sea level on most anthropogenic coasts. In that frame, the CHERbourg bLOC (CHERLOC) project aims to study new coastal engineering solutions (overtopping, sediment transport) thanks to two new artificial units in two test sites (Normandy, France) considering biodiversity preservation but also societal acceptability. This study details an efficient method to monitor such coastal infrastructure using terrestrial Structure from Motion (SfM). In 2021, surveys were conducted to acquire pictures in April, May, June and November. A time series of 3D photogrammetric models was generated using open source SfM software. The first model was georeferenced using Ground Control Points (GCP) measured by Differential Global Navigation Satellite System (DGNSS) so that it could be used as a reference for the following point clouds using surrounding ripraps assumed to be non-mobile through the period of the study. The georeferencing Root Mean Square Error (RMSE) was found to be 1.8 cm for the April model whereas RMSEs of relative registrations of the following dates were found to be sub-centimetric. These results can be used to observe and measure blocks displacements as well as sand volumes evolution throughout the time series. The biggest displacement was found to be 23 cm between April and June. Sand topographic variation shows a continuous accumulation on selected cross-sections between April and November with an overall height accumulation of about 30 cm. Sand volumes measurements show consistent results with an added volume of 3.67 m3 on the previous areas. Numéro de notice : A2022-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-359-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-359-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100734
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 359 - 366[article]Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkLandslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)PermalinkDeep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)PermalinkOptimal resolution of soil properties maps varies according to their geographical extent and location / Christian Piedallu in Geoderma, vol 412 (15 April 2022)PermalinkAssessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity / Stéphane Bertin in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkCoupling fossil records and traditional discrimination metrics to test how genetic information improves species distribution models of the European beech Fagus sylvatica / Pedro Poli in European Journal of Forest Research, vol 141 n° 2 (April 2022)PermalinkQuantifying discrepancies in the three-dimensional seasonal variations between IGS station positions and load models / Yujiao Niu in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkVolunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)PermalinkAre northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (March-1 2022)PermalinkFeasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkInfluence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkMonitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkUnderstanding the geodetic signature of large aquifer systems: Example of the Ozark plateaus in central United States / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 127 n° 3 (March 2022)PermalinkSuspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAssessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria / Aida Bensekhria in ISPRS International journal of geo-information, vol 11 n° 2 (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)PermalinkSeasonal variations of vertical crustal motion in Australia observed by joint analysis of GPS and GRACE / Hao Wang in Geomatics and Information Science of Wuhan University, vol 47 n° 2 (February 2022)PermalinkSiamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)PermalinkUsing vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkCo-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkForest floor alteration by canopy trees and soil wetness drive regeneration of a spruce-beech forest / Pavel Daněk in Forest ecology and management, vol 504 (January-15 2022)PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkAnalyse haute résolution de la morphologie des paysages et des processus à partir de LiDAR aéroporté répété et simulation hydraulique / Thomas Bernard (2022)PermalinkPermalinkPermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)PermalinkEstimation of Lesser Antilles vertical velocity fields using a GNSS-PPP software comparison / Pierre Sakic-Kieffer (2022)PermalinkÉvolution rétrospective et prospective d’un massif dunaire par imagerie multispectrale et LiDAR / Iris Jeuffrard (2022)PermalinkFungal perspective of pine and oak colonization in Mediterranean degraded ecosystems / Irene Adamo in Forests, vol 13 n° 1 (January 2022)PermalinkA GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkHistorical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)PermalinkLandslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)PermalinkModalités et rythmes d'évolution des falaises des Vaches Noires (Normandie, France) : caractérisation et quantification des dynamiques hydrogravitaires par approches multi-scalaires / Thomas Roulland (2022)PermalinkModélisation du lien entre éruptions et glissements de flancs au Piton de la Fournaise / Quentin Dumont (2022)PermalinkA prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkA rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkSalt tectonic imaging at crustal and experimental scales by seismic migration and adjoint method / Javier Abreu-Torres (2022)PermalinkThree-dimensional simulations of rockfalls in Ischia, Southern Italy, and preliminary susceptibility zonation / Massimiliano Alvioli in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkPermalinkA comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkGIS to identify exposed shoreline sectors to wave impacts: case of El Tarf coast / Abdeldjalil Goumrasa in Applied geomatics, vol 13 n° 4 (December 2021)PermalinkLithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkMetamorphic transformation rate over large spatial and temporal scales constrained by geophysical data and coupled modelling / Gyorgy Hetényl in Journal of metamorphic geology, vol 39 n° 9 (December 2021)PermalinkOBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)PermalinkWhat is the impact of tectonic plate movement on country size? A long-term forecast / Kamil Maciuk in Remote sensing, vol 13 n° 23 (December-1 2021)Permalink