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Local terrain modification method considering physical feature constraints for vector elements / Jiangfeng She in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 5 (September 2020) . - pp 452 - 470 Titre : Local terrain modification method considering physical feature constraints for vector elements Type de document : Article/Communication Auteurs : Jiangfeng She, Auteur ; Junyan Liu, Auteur ; Junzhong Tan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 452 - 470 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] altitude [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] contrainte d'intégrité [Termes descripteurs IGN] déformation de surface [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] rastérisation [Termes descripteurs IGN] relief [Termes descripteurs IGN] superposition de données [Termes descripteurs IGN] surface du sol [Termes descripteurs IGN] terrain [Termes descripteurs IGN] traitement parallèle [Termes descripteurs IGN] zone tampon Résumé : (auteur) Many studies have been focused on rendering 2D vector elements on 3D terrain, and a series of algorithms have been proposed. Most of these algorithms struggle to provide a seamless overlay between vector elements and an irregular terrain surface. Despite their importance, the physical characteristics of vector elements are often ignored, which distorts the surface of vector elements. For example, if vector elements that represent roads and rivers are simply overlaid on terrain, the phenomena of uneven surfaces and rivers going uphill may occur because of elevation fluctuation. To correct these deficiencies, terrain should be modified according to the physical characteristics of vectors. We propose a local terrain modification method: First, the elevation of terrain covered by vector elements is recalculated according to vectors’ physical characteristics. Second, the multigrid method is used to realize a smooth transition between the modified terrain and its surrounding area. Finally, by setting different transition ranges and comparing the visualization effects, rules are given for the selection of a suitable range. After modification, the terrain conforms to vectors’ physical characteristics, and the overall relief is undamaged. The proposed method was applied to a CPU–GPU parallel heterogeneous model and demonstrated a high level of performance. Numéro de notice : A2020-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1770128 date de publication en ligne : 06/07/2020 En ligne : https://doi.org/10.1080/15230406.2020.1770128 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95660 [article]

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A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 2994 - 3006 Titre : A point cloud feature regularization method by fusing judge criterion of field force Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Qing Liu, Auteur ; Keguen Yu, Auteur Année de publication : 2020 Article en page(s) : pp 2994 - 3006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] niveau de gris (image) [Termes descripteurs IGN] partitionnement binaire [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] reconstruction d'objet [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] spline cubique [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] Wuhan (Chine) Résumé : (auteur) Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved $k$ - $d$ tree method is used to search the $k$ neighbors of sampling point. Then, the sampling point and its $k$ neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology. Numéro de notice : A2020-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946326 date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2946326 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94968 [article]

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 5 (May 2020) Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] algorithme espérance-maximisation [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] autorégression [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] corrélation croisée normalisée [Termes descripteurs IGN] erreur aléatoire [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station GPS [Termes descripteurs IGN] valeur aberrante Résumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120 [article]

Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 40 n°19 (February 2019) . - pp 7439 - 7458 Titre : Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors Type de document : Article/Communication Auteurs : Pauline Perbet, Auteur ; Michelle Fortin, Auteur ; Anouk Ville, Auteur ; Martin Béland, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 7439 - 7458 Note générale : bibliographie This work was supported by the Natural Sciences and Engineering Research Council of Canada. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] défrichement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Indonésie [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] surveillance forestière Résumé : (auteur) Malaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process. Numéro de notice : A2019-321 Affiliation des auteurs : ENSG+Ext Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1579390 date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/01431161.2019.1579390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93295 [article]

Computing with cognitive spatial frames of reference in GIS / Simon Scheider in Transactions in GIS, vol 22 n° 5 (October 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 5 (October 2018) . - pp 1083 - 1104 Titre : Computing with cognitive spatial frames of reference in GIS Type de document : Article/Communication Auteurs : Simon Scheider, Auteur ; Jürgen Hahn, Auteur ; Paul Weiser, Auteur ; Werner Kuhn, Auteur Année de publication : 2018 Article en page(s) : pp 1083 - 1104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] espace vectoriel [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] modèle cognitif [Termes descripteurs IGN] transformation géométrique Résumé : (Auteur) In everyday communication, people effortlessly translate between spatial cognitive frames of reference. For example, a tourist guide translates from a map (“the fountain is north‐west of the church”) into a cognitive frame for a tourist (“the fountain in front of the church”). While different types of cognitive reference frames and their relevance for language cultures have been studied in considerable depth, we still lack adequate transformation models. In this article, we argue that transformations in current Geographic Information Systems (GIS) are inappropriate to this end. Appropriate transformation models need to go beyond point discretization to take into account vague transformations, in order to deal with forms, sizes, and vagueness of spatial relations relative to ground objects. We argue that neural fields should be used to denote fuzzy positions, directions, and sizes in a particular frame. We propose fuzzy vector spaces to approximate neural field behavior with affine transformations, including fuzzy translation, rotation, and scaling, in order to efficiently transform between different cognitive perspectives. We use an implementation in Haskell to describe a geographic map from the perspective of six well‐known cognitive frames of reference. Based on these findings, we give an outlook on the principles of a “neural GIS.” Numéro de notice : A2018-570 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12318 date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.1111/tgis.12318 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92289 [article]

Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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A method for assessing generalized data accuracy with linear object resolution verification / Tadeusz Chrobak in Geocarto international, vol 32 n° 3 (March 2017)  

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Location K-anonymity in indoor spaces / Joon-Seok Kim in Geoinformatica [en ligne], vol 20 n° 3 (July - September 2016)  

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Propagation des ondes électromagnétiques dans l’atmosphère terrestre / Jonathan Chenal (2016) 

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QGIS 2 cookbook / Alex Mandel (2016)

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Mappes et temporalités : la logique mappologique à l'épreuve de la topochronie / Régis Keerle in Cartes & Géomatique, n° 225 (septembre 2015) 

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On reverse-k-nearest-neighbor joins / Tobias Emrich in Geoinformatica [en ligne], vol 19 n° 2 (April - June 2015)  

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Efficient continuous top-k spatial keyword queries on road networks / Long Guo in Geoinformatica [en ligne], vol 19 n° 1 (January - March 2015)  

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Les réseaux techniques comme vecteurs de distribution des risques en milieu urbain / Serge Lhomme in Cartes & Géomatique, n° 215 (mars 2013)  

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Query-aware location anonymization for road networks / C. Chow in Geoinformatica, vol 15 n° 3 (July 2011)  

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