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A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 321 - 347 Titre : A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Zhice Fang, Auteur ; Yi Wang, Auteur ; Ling Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 321 - 347 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] pondération [Termes descripteurs IGN] régression logistique [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] risque naturel Résumé : (auteur) This study introduces four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine, and logistic regression in specific ways to produce reliable results and avoid problems with the model selection. The study consists of three main steps. The first step establishes a spatial database consisting of 16 landslide conditioning factors and 380 historical landslide locations. The second step randomly selects training (70% of the total) and test (30%) datasets out of grid cells corresponding to landslide and non-slide locations in the study area. The final step constructs the proposed heterogeneous ensemble-learning methods for landslide susceptibility mapping. The proposed ensemble-learning methods show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures. The blending ensemble-learning method achieves the highest overall accuracy of 80.70% compared to the other ensemble-learning methods. Numéro de notice : A2021-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808897 date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808897 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96704 [article]

Automated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM / Samy Ismail Elmahdy in Geocarto international, vol 36 n° 1 ([01/01/2021])  

Public [article]  inGeocarto international > vol 36 n° 1 [01/01/2021] . - pp 76 - 95 Titre : Automated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM Type de document : Article/Communication Auteurs : Samy Ismail Elmahdy, Auteur ; Mohamed Mostafa Mohamed, Auteur ; Tarig A Ali, Auteur Année de publication : 2021 Article en page(s) : pp 76 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte géologique [Termes descripteurs IGN] linéament [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] milieu tropical [Termes descripteurs IGN] MNS SRTM [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] structure géologique Résumé : (Auteur) The availability of the large volume of remote sensing data has allowed for the developing of several automated algorithms for detecting linear geological features and more reliable analysis. However, most of the algorithms focus on edge detection and tone change on a satellite image, which represents all geological and non-geological features. In this study, a topographic fabric algorithm, which calculates the slope and aspect at each point in a DEM, is applied for automatically geological linear features mapping in Bau Goldfield, Malaysia using the new version of the Shuttle Radar Topographic Mission (SRTM) DEM. A series of topographic fabric input parameters was tested using different combinations of input values in order to decide the optimal parameters that provided the suitable detection parameters, best fit and the highest accuracy. Comparison with the geological map demonstrated that the tested parameters made the algorithm able to automatically detect geological structures. Numéro de notice : A2021-052 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1594393 date de publication en ligne : 29/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1594393 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96776 [article]

Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions / Kamil Maciuk in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 61 - 73 Titre : Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions Type de document : Article/Communication Auteurs : Kamil Maciuk, Auteur ; Inese Vārna, Auteur ; Chang Xu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] Lettonie [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station de référence [Termes descripteurs IGN] variance de Hadamard [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Long term GNSS observations provided by networks of the continuously operating reference stations (CORS) allow for determination of the global and local tectonic plate movements and seasonal variations. In recent years, PPP (Precise Point Positioning) technique has become increasingly popular and most likely in the future will replace relative positioning with CORS stations. In this paper, we discuss the difference of the velocity and seasonal component estimates of 25 Latvian CORS stations on the basis of daily PPP solutions from the Nevada Geodetic Laboratory and double-difference solutions from the Institute of Geodesy and Geoinformatics of the University of Latvia. Time series of each coordinate component for 9-year time period were determined by the usage of the Tsview software and seasonal existence of linear, annual, semi-annual factors and their uncertainties were determined. Breaks (e. g., antenna and receiver changes) were also taken into account. We then assessed the noise characteristics of these time series with the use of overlapping Hadamard variance (OHVAR). The result shows that OHVAR is computationally cheap, and the dominating power-law noise, including flicker and random walk. However Hadamard deviation of the PPP and double-difference solutions scatters differently for a whole year averaging time due to the different GNSS data strategies. Numéro de notice : A2021-045 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0042 date de publication en ligne : 08/12/2020 En ligne : https://doi.org/10.1515/jag-2020-0042 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96772 [article]

Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)  

Public [article]  inComputers & geosciences > vol 146 (January 2021) . - n° 104626 Titre : Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs Type de document : Article/Communication Auteurs : Yang Bai, Auteur ; Maojin Tan, Auteur Année de publication : 2021 Article en page(s) : n° 104626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] puits de carbone [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] schiste Résumé : (auteur) The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with complex data. The neural network method can improve prediction accuracy, but it always generates unstable prediction models. A static committee machine can reduce errors and uncertainties by combining multiple learners, but the weight of integrating learners is difficult to determine. Therefore, a dynamic committee machine with fuzzy-c-means clustering (DCMF) was proposed to predict the TOC content. Experts in the DCMF include Elman neural network, extreme learning machine, and generalized regression neural network. The fuzzy-c-means clustering algorithm was used as the gate network to perform subtasks decomposition and weights calculation based on input data. The subtasks were used to train more adaptive TOC content prediction models, and the weights were transferred to the combiner to integrate all experts’ outputs into final results. The DCMF was applied in two wells located in the Jiumenchong formation in the Qiannan depression, China. The TOC prediction results using the DCMF method are more accurate than the linear regression method, three individual intelligent algorithms, and the static committee machine. The DCMF also provides a new method for weight calculation by mining potential information of input data. Numéro de notice : A2021-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2020.104626 date de publication en ligne : 17/10/2020 En ligne : https://doi.org/10.1016/j.cageo.2020.104626 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96512 [article]

Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 31 - 45 Titre : Fusion of ground penetrating radar and laser scanning for infrastructure mapping Type de document : Article/Communication Auteurs : Dominik Merkle, Auteur ; Carsten Frey, Auteur ; Alexander Reiterer, Auteur Année de publication : 2021 Article en page(s) : pp 31 - 45 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] espace de Hilbert [Termes descripteurs IGN] lasergrammétrie [Termes descripteurs IGN] lever souterrain [Termes descripteurs IGN] Radar pénétrant GPR [Termes descripteurs IGN] radargrammétrie [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] sous-sol [Termes descripteurs IGN] surface du sol [Termes descripteurs IGN] système de numérisation mobile Résumé : (auteur) Mobile mapping vehicles, equipped with cameras, laser scanners (in this paper referred to as light detection and ranging, LiDAR), and positioning systems are limited to acquiring surface data. However, in this paper, a method to fuse both LiDAR and 3D ground penetrating radar (GPR) data into consistent georeferenced point clouds is presented, allowing imaging both the surface and subsurface. Objects such as pipes, cables, and wall structures are made visible as point clouds by thresholding the GPR signal’s Hilbert envelope. The results are verified with existing utility maps. Varying soil conditions, clutter, and noise complicate a fully automatized approach. Topographic correction of the GPR data, by using the LiDAR data, ensures a consistent ground height. Moreover, this work shows that the LiDAR point cloud, as a reference, increases the interpretability of GPR data and allows measuring distances between above ground and subsurface structures. Numéro de notice : A2021-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0004 date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1515/jag-2020-0004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96771 [article]

Retrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January 2021)  

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Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January 2021)  

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The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January 2021)  

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Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (December 2020)  

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Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)  

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Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)

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Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

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Displacement monitoring of upper Atbara dam based on time series InSAR / Q.Q. Wang in Survey review, vol 52 n° 375 (November 2020)  

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Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Macrozonation of seismic transient and permanent ground deformation of Iran / Saeideh Farahani in Natural Hazards and Earth System Sciences, vol 20 n° 11 (November 2020)  

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