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PCEDNet: 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)  

Public [article]  inACM Transactions on Graphics, TOG > Vol 41 n° 1 (February 2022) . - n° 10 Titre : PCEDNet: a lightweight neural network for fast and interactive edge detection in 3D point clouds Type de document : Article/Communication Auteurs : Chems-Eddine Himeur, Auteur ; Thibault Lejemble, Auteur ; Thomas Pellegrini, Auteur ; Mathias Paulin, Auteur ; Loïc Barthe, Auteur ; Nicolas Mellado, Auteur Année de publication : 2022 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] matrice [Termes IGN] semis de points Résumé : (auteur) In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation, and classification. In this article, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well-suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time, and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train, and classifies millions of points in seconds. Numéro de notice : A2022-304 Affiliation des auteurs : non IGN Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1145/3481804 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1145/3481804 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100374 [article]

The method of detection and localization of configuration defects in geodetic networks by means of Tikhonov regularization / Roman Kadaj in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)  

Public [article]  inReports on geodesy and geoinformatics > vol 112 n° 1 (December 2021) . - pp 19 - 25 Titre : The method of detection and localization of configuration defects in geodetic networks by means of Tikhonov regularization Type de document : Article/Communication Auteurs : Roman Kadaj, Auteur Année de publication : 2021 Article en page(s) : pp 19 - 25 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] détection d'erreur [Termes IGN] matrice inversible [Termes IGN] régularisation de Tychonoff [Termes IGN] réseau géodésique [Termes IGN] valeur aberrante Résumé : (auteur) In adjusted geodetic networks, cases of local configuration defects (defects in the geometric structure of the network due to missing data or errors in point numbering) can be encountered, which lead to the singularity of the normal equation system in the least-squares procedure. Numbering errors in observation sets cause the computer program to define the network geometry incorrectly. Another cause of a defect may be accidental omission of certain data records, causing local indeterminacy or lowering of local reliability rates in a network. Obviously, the problem of a configuration defect may be easily detectable in networks with a small number of points. However, it becomes a real problem in large networks, where manual checking of all data becomes a very expensive task. The paper presents a new strategy for the detection of configuration defects with the use of the Tikhonov regularization method. The method was implemented in 1992 in the GEONET system (www.geonet.net.pl). Numéro de notice : A2021-961 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.2478/rgg-2021-0004 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.2478/rgg-2021-0004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100113 [article]

MSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 12 (December 2021) . - pp 901 - 906 Titre : MSegnet, a practical network for building detection from high spatial resolution images Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Fang Chen, Auteur ; Ying Dong, Auteur Année de publication : 2021 Article en page(s) : pp 901 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] matrice [Termes IGN] segmentation multi-échelle [Termes IGN] segmentation sémantique Résumé : (Auteur) Building detection in big earth data by remote sensing is crucial for urban development. However, improving its accuracy remains challenging due to complicated background objects and different viewing angles from various remotely sensed images. The hereto proposed methods predominantly focus on multi-scale feature learning, which omits features in multiple aspect ratios. Moreover, postprocessing is required to refine the segmentation performance. We propose modified semantic segmentation (MSegnet), a single-shot semantic segmentation model based on a matrix of convolution layers to extract features in multiple scales and aspect ratios. MSegnet consists of two modules: backbone feature learning and matrix convolution to conduct vertical and horizontal learning. The matrix convolution comprises a set of convolution operations with different aspect ratios. MSegnet is applied to a public building data set that is widely used for evaluation and shown to achieve satisfactory accuracy, compared with the published single-shot methods. Numéro de notice : A2021-898 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00016R2 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.14358/PERS.21-00016R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99296 [article]

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Least squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 717 - 733 Titre : Least squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing Type de document : Article/Communication Auteurs : Radhika Ravi, Auteur ; Ayman Habib, Auteur Année de publication : 2021 Article en page(s) : pp 717 - 733 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] alignement des données [Termes IGN] chevauchement [Termes IGN] compensation par moindres carrés [Termes IGN] données lidar [Termes IGN] lidar mobile [Termes IGN] matrice [Termes IGN] matrice de covariance [Termes IGN] modèle de Gauss-Helmert [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle mathématique [Termes IGN] modélisation 3D [Termes IGN] pondération [Termes IGN] semis de points Résumé : (Auteur) This article proposes a solution to special least squares adjustment (LSA) models with a rank-deficient weight matrix, which are commonly encountered in geomatics. The two sources of rank deficiency in weight matrices are discussed: naturally occurring due to the inherent characteristics of LSA mathematical models and artificially induced to eliminate nuisance parameters from LSA estimation. The physical interpretation of the sources of rank deficiency is demonstrated using a case study to solve the problem of 3D line fitting, which is often encountered in geomatics but has not been addressed fully to date. Finally, some geomatics-related applications—mobile lidar system calibration, point cloud registration, and single-photo resection—are discussed along with respective experimental results, to emphasize the need to assess LSA models and their weight matrices to draw inferences regarding the effective contribution of observations. The discussion and results demonstrate the vast applications of this research in geomatics as well as other engineering domains. Numéro de notice : A2021-675 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00081R3 Date de publication en ligne : 10/01/2021 En ligne : https://doi.org/10.14358/PERS.20-00081R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98861 [article]

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New algorithms for spherical harmonic analysis of area mean values over blocks delineated by equiangular and Gaussian grids / Rong Sun in Journal of geodesy, vol 95 n° 5 (May 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 5 (May 2021) . - n° 47 Titre : New algorithms for spherical harmonic analysis of area mean values over blocks delineated by equiangular and Gaussian grids Type de document : Article/Communication Auteurs : Rong Sun, Auteur Année de publication : 2021 Article en page(s) : n° 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse harmonique [Termes IGN] grille [Termes IGN] matrice [Termes IGN] méthode des moindres carrés [Termes IGN] quadrature [Termes IGN] transformation polynomiale Résumé : (auteur) Spherical harmonic analysis is widely used in all aspects of geoscience. Exact quadrature methods are available for the spherical harmonic analysis of band-limited point values at the grid points of equiangular and Gaussian grids. However, no similarly exact quadrature methods are available for the spherical harmonic analysis of area mean values over the blocks delineated by these grids. In this study, new algorithms appropriate for the exact spherical harmonic analysis of the band-limited area mean values over the blocks delineated by equiangular and Gaussian grids are proposed. For band-limited data, precision that is between that of the least-squares estimation method and of the approximate quadrature methods can be achieved by using the new algorithms. Regarding the computational complexity, fewer operations are needed by the new methods as compared to those needed by the least-squares estimation method and the approximate quadrature methods in the preparation stage when the maximum degree of the spherical harmonic analysis is very large. Simulation experiments are performed to compare the ability to recover the spherical harmonic coefficients by using the least-squares estimation method, the approximate quadrature methods and these new algorithms from aliased data with aliasing components of realistic magnitudes. The results suggest that these new algorithms, with time complexity one order less than that of the least-squares estimation method in the solving stage, perform roughly the same as the least-squares estimation method in recovering spherical harmonic coefficients from the aliased data. Numéro de notice : A2021-488 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01495-8 Date de publication en ligne : 07/04/2021 En ligne : https://doi.org/10.1007/s00190-021-01495-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97507 [article]

Parallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)  

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Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm / Fengfan Wang in Computers & geosciences, vol 149 (April 2021)  

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A practical method for calculating reliable integer float estimator in GNSS precise positioning / Xianwen Yu in Survey review, Vol 53 n° 377 (February 2021)  

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A robust total Kalman filter algorithm with numerical evaluation / Sida Li in Survey review, vol 52 n° 373 (July 2020)  

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A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Progress towards a rigorous error propagation for total least-squares estimates / Burkhard Schaffrin in Journal of applied geodesy, vol 14 n° 2 (April 2020)  

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The stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment / Gaël Kermarrec in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

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Jacques Bertin’s legacy in information visualization and the reorderable matrix / Charles Perin in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)  

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The characteristics of asymmetric pedestrian behavior : A preliminary study using passive smartphone location data / Nick Malleson in Transactions in GIS, vol 22 n° 2 (April 2018)  

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