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Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])
[article]
Titre : Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys Type de document : Article/Communication Auteurs : Ashutosh Tiwari, Auteur ; Avadh Bihari Narayan, Auteur ; Ramji Dwivedi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 535 - 558 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arpentage
[Termes IGN] corrélation croisée maximale
[Termes IGN] covariance
[Termes IGN] données GNSS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effondrement de terrain
[Termes IGN] escarpement
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle numérique de surface
[Termes IGN] précipitation
[Termes IGN] surveillance géologique
[Termes IGN] tachéomètre électronique robotiséRésumé : (auteur) A robust geodetic framework comprising Terrestrial Laser Scanner (TLS), Global Navigation Satellite Systems (GNSS), Robotic Total Station (RTS) and Multi-temporal InSAR (MT-InSAR) was employed first in India to investigate a landslide-prone Sirobagarh region, Uttarakhand, at different spatial extents, and to evaluate the relationship amongst the displacement estimates obtained from the applied surveying techniques. TLS derived digital elevation models indicated displacements >5 m on the landslide upper scarp. GNSS- and RTS-based observations showed horizontal movements towards the Alaknanda river in the landslide slope direction (maximum values: 0.1305 and 0.045 m, respectively), and downward vertical motion (largest subsidence magnitude: −2.1315 and −0.030 m, respectively). MT-InSAR processing of Sentinel-1a images identified 21071 measurement pixels, highlighting subsidence around the landslide (mean velocity range: −0.110 to 0.008 m/year). Analysis of displacement vectors using vector equality, cross-covariance, cross-correlation and principal component analysis reveals that GNSS vertical displacement estimates were partially correlated with MT-InSAR measurements (correlated for epoch difference 2–3), whereas there was good cross-correlation between MT-InSAR and LiDAR observations throughout. The displacement estimates and their analyses evident unstable movement of the landslide scarp occurring due to debris flow and rainfall, and a relatively moderate subsidence activity in the surrounding areas lying in the landslide zone. Numéro de notice : A2020-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524516 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524516 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94770
in Geocarto international > vol 35 n° 5 [01/04/2020] . - pp 535 - 558[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Multitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data / Tomás Fernández in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
[article]
Titre : Multitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data Type de document : Article/Communication Auteurs : Tomás Fernández, Auteur ; José Luis Pérez-García, Auteur ; José Miguel Gómez-López, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 30 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] Andalousie
[Termes IGN] données lidar
[Termes IGN] données publiques
[Termes IGN] érosion
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] Olea europaea
[Termes IGN] orthophotographie
[Termes IGN] point d'appui
[Termes IGN] précipitation
[Termes IGN] ravin
[Termes IGN] semis de pointsRésumé : (auteur) Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape. Numéro de notice : A2020-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040260 Date de publication en ligne : 19/04/2020 En ligne : https://doi.org/10.3390/ijgi9040260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95029
in ISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 30 p.[article]Assessment of dense image matchers for digital surface model generation using airborne and spaceborne images – an update / Yilong Han in Photogrammetric record, vol 35 n° 169 (March 2020)
[article]
Titre : Assessment of dense image matchers for digital surface model generation using airborne and spaceborne images – an update Type de document : Article/Communication Auteurs : Yilong Han, Auteur ; Rongjun Qin, Auteur ; Xu Huang, Auteur Année de publication : 2020 Article en page(s) : pp 58 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement d'images
[Termes IGN] estimation de précision
[Termes IGN] image captée par drone
[Termes IGN] image satellite
[Termes IGN] modèle numérique de surfaceRésumé : (Auteur) Digital surface model (DSM) generation is one of the fundamental issues in photogrammetry and the mapping industry. This paper provides a comprehensive assessment of state‐of‐the‐art image matchers using nine open‐source and commercial software packages on aerial and unmanned aerial vehicle (UAV) images and five software packages on spaceborne images. Two datasets provide an update on DSM generation software for both airborne and spaceborne data: a 5 × 5 UAV image block with high‐precision models; and a WorldView‐1 stereopair with lidar reference data. To understand the performance of the image matchers, accuracy analysis is additionally performed on five selected ground objects. The tested image matchers adopting hierarchical semi‐global matching fitted the reference DSM better, thus yielding better accuracy. Numéro de notice : A2020-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12310 Date de publication en ligne : 29/03/2020 En ligne : https://doi.org/10.1111/phor.12310 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94816
in Photogrammetric record > vol 35 n° 169 (March 2020) . - pp 58 - 80[article]Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)
[article]
Titre : Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network Type de document : Article/Communication Auteurs : Yueguan Yan, Auteur ; Haixu Yan, Auteur ; Junting Guo, Auteur ; Huayang Dai, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] objet 3D
[Termes IGN] reconnaissance d'objets
[Termes IGN] semis de points clairsemésRésumé : (auteur) The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial–local correlation and plays an essential role in the process of sparse point cloud recognition. Numéro de notice : A2020-256 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi9030182 Date de publication en ligne : 24/03/2020 En ligne : https://doi.org/10.3390/ijgi9030182 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95012
in ISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 19 p.[article]Deep learning for geometric and semantic tasks in photogrammetry and remote sensing / Christian Helpke in Geo-spatial Information Science, vol 23 n° 1 (March 2020)
[article]
Titre : Deep learning for geometric and semantic tasks in photogrammetry and remote sensing Type de document : Article/Communication Auteurs : Christian Helpke, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2020 Article en page(s) : pp 10 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image aérienne
[Termes IGN] intelligence artificielle
[Termes IGN] photogrammétrie numérique
[Termes IGN] télédétectionRésumé : (auteur) During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development. Numéro de notice : A2020-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2020.1718003 Date de publication en ligne : 03/02/2020 En ligne : https://doi.org/https://doi.org/10.1080/10095020.2020.1718003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94821
in Geo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 10 - 19[article]Efficient match pair selection for oblique UAV images based on adaptive vocabulary tree / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkEstimation of variance and spatial correlation width for fine-scale measurement error in digital elevation model / Mikhail L. Uss in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkIntegrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images / Wen Dai in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)PermalinkIntegration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkA novel method of spatiotemporal dynamic geo-visualization of criminal data, applied to command and control centers for public safety / Mayra Salcedo-Gonzalez in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkVariable DEM generalization using local entropy for terrain representation through scale / Paulo Raposo in International journal of cartography, Vol 6 n° 1 (March 2020)PermalinkA LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams / Benjamin Swan in Transactions in GIS, Vol 24 n° 1 (February 2020)PermalinkSome thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkStatistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition / Ademir Marques Junior in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkThe "Incense Road" from Petra to Gaza: an analysis using GIS and Cost functions / Motti Zohar in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)PermalinkThree-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkCombining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkModelling the orthoimage accuracy using DEM accuracy and off-nadir angle / Altan Yilmaz in Geocarto international, Vol 35 n° 1 ([02/01/2020])PermalinkSpatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])PermalinkPermalink3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)PermalinkAnalyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020)PermalinkPermalinkAssessment of ArcGIS based extraction of geoidal undulation compared to National Geospatial Intelligence Agency (NGA) model – A case study / Sher Muhammad in Journal of applied geodesy, vol 14 n° 1 (January 2020)PermalinkAutomatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)Permalink