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Termes IGN > mathématiques > statistique mathématique
statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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A network-constrained clustering method for bivariate origin-destination movement data / Wenkai Liu in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)
[article]
Titre : A network-constrained clustering method for bivariate origin-destination movement data Type de document : Article/Communication Auteurs : Wenkai Liu, Auteur ; Qiliang Liu, Auteur ; Jie Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 767 - 787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse bivariée
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] origine - destination
[Termes IGN] réseau routierRésumé : (auteur) For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services. Numéro de notice : A2023-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2137879 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2137879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103108
in International journal of geographical information science IJGIS > vol 37 n° 4 (April 2023) . - pp 767 - 787[article]Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)
[article]
Titre : Towards global scale segmentation with OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Munazza Usmani, Auteur ; Maurizio Napolitano, Auteur ; Francesca Bovolo, Auteur Année de publication : 2023 Article en page(s) : n° 100031 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données localisées des bénévoles
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du solRésumé : (auteur) Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications. Numéro de notice : A2023-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100031 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102807
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031[article]A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments / Bo Xu in GPS solutions, vol 27 n° 2 (April 2023)
[article]
Titre : A unified cycle-slip, multipath estimation, detection and mitigation method for VIO-aided PPP in urban environments Type de document : Article/Communication Auteurs : Bo Xu, Auteur ; Shoujian Zhang, Auteur ; Kaifa Kuang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] filtre de Kalman
[Termes IGN] glissement de cycle
[Termes IGN] milieu urbain
[Termes IGN] modèle stochastique
[Termes IGN] navigation autonome
[Termes IGN] navigation inertielle
[Termes IGN] odomètre
[Termes IGN] phase
[Termes IGN] positionnement ponctuel précis
[Termes IGN] signal GNSS
[Termes IGN] trajet multipleRésumé : (auteur) Accurate, continuous and reliable positioning is required in autonomous driving. The precise point positioning (PPP) technique, which can provide a global accurate positioning service using a single global navigation satellite system (GNSS) receiver, has attracted much attention. Nevertheless, due to the cycle slips and multipath effects in the GNSS signal, the performance of PPP is severely degraded in urban areas, which has a negative effect on the PPP/inertial navigation system (INS)/vision integrated navigation. Moreover, the carrier phase observations with un-modeled multipath cause false detection of small cycle slips and lead to deviation in the state variable estimation in PPP. Therefore, an effective cycle slip/multipath estimation, detection and mitigation (EDM) method is proposed. A clustering method is used to separate the cycle slips and multipath from the carrier phase observations aided by visual inertial odometry (VIO) positioning results. The influence of the carrier phase multipath on state variable estimation is reduced by adjusting the stochastic ambiguity model in the Kalman filter. The proposed EDM method is validated by vehicle experiments conducted in urban and freeway areas. Experimental results demonstrate that 0.2% cycle slip detection error is achieved by our method. Besides, the multipath estimation accuracy of EDM improves by more than 50% compared with the geometry-based (GB) method. Regarding positioning accuracy, the EDM method has a maximum of 72.2% and 63.2% improvement compared to traditional geometry-free (GF) and GB methods. Numéro de notice : A2023-124 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-023-01396-7 Date de publication en ligne : 17/01/2023 En ligne : https://doi.org/10.1007/s10291-023-01396-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102501
in GPS solutions > vol 27 n° 2 (April 2023) . - n° 59[article]Determination of Helmert transformation parameters for continuous GNSS networks: a case study of the Géoazur GNSS network / Dinh Trong Tran in Geo-spatial Information Science, vol 26 n° 1 (March 2023)
[article]
Titre : Determination of Helmert transformation parameters for continuous GNSS networks: a case study of the Géoazur GNSS network Type de document : Article/Communication Auteurs : Dinh Trong Tran, Auteur ; Jean-Mathieu Nocquet , Auteur ; Ngoc Dung Luong, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 125 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] données GNSS
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] méthode des moindres carrés
[Termes IGN] transformation de coordonnées
[Termes IGN] transformation de Helmert
[Termes IGN] valeur aberranteRésumé : (auteur) In this paper, we propose an approach to determine seven parameters of the Helmert transformation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984 (WGS84) to the International Terrestrial Reference Frame. This includes (1) converting the coordinates of common points from the global coordinate system to the local coordinate system, (2) identifying and eliminating outliers by the Dikin estimator, and (3) estimating seven parameters of the Helmert transformation by least squares (LS) estimation with the “clean” data (i.e. outliers removed). Herein, the local coordinate system provides a platform to separate points’ horizontal and vertical components. Then, the Dikin estimator identifies and eliminates outliers in the horizontal or vertical component separately. It is significant because common points in a continuous GNSS network may contain outliers. The proposed approach is tested with the Géoazur GNSS network with the results showing that the Dikin estimator detects outliers at 6 out of 18 common points, among which three points are found with outliers in the vertical component only. Thus, instead of eliminating all coordinate components of these six common points, we only eliminate all coordinate components of three common points and only the vertical component of another three common points. Finally, the classical LS estimation is applied to “clean” data to estimate seven parameters of the Helmert transformation with a significant accuracy improvement. The Dikin estimator’s results are compared to those of other robust estimators of Huber and Theil-Sen, which shows that the Dikin estimator performs better. Furthermore, the weighted total least-squares estimation is implemented to assess the accuracy of the LS estimation with the same data. The inter-comparison of the seven estimated parameters and their standard deviations shows a small difference at a few per million levels (E-6). Numéro de notice : A2023-208 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/10095020.2022.2138569 Date de publication en ligne : 15/11/2022 En ligne : https://doi.org/10.1080/10095020.2022.2138569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103135
in Geo-spatial Information Science > vol 26 n° 1 (March 2023) . - pp 125 - 138[article]Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)
[article]
Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte ancienne
[Termes IGN] cartographie historique
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] données anciennes
[Termes IGN] matrice de co-occurrence
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] vision par ordinateurRésumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804
in ISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211[article]Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkSeismic deformation in the Adriatic Sea region / B. Orecchio in Journal of geodynamics, vol 155 (March 2023)PermalinkSpecies distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal / Cristina Alegria in Forests, vol 14 n° 3 (March 2023)PermalinkThe potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)PermalinkA unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)PermalinkA spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil / Jiawei Liu in Science of the total environment, vol 859 n° 1 (February 2023)PermalinkComparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)PermalinkGenerating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network / Jingan Wu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkLarge-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkMeasuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)Permalink