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Titre : Stochastic models for geodesy and geoinformation science Type de document : Monographie Auteurs : Frank Neitzel, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 200 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-982-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] B-Spline
[Termes IGN] covariance
[Termes IGN] erreur de phase
[Termes IGN] incertitude de mesurage
[Termes IGN] interférométrie à très grande base
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] phase GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] réseau géodésique
[Termes IGN] télémétrie laser aéroportéRésumé : (éditeur) In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena. Note de contenu : 1- Total least-squares collocation: An optimal estimation technique for the EIV-model with prior information
2- Weighted Total Least Squares (WTLS) Solutions for straight line fitting to 3D point data
3- Stochastic properties of confidence ellipsoids after least squares adjustment, derived from GUM analysis and Monte Carlo simulations
4- Mean shift versus variance inflation approach for outlier detection—A comparative study
5- A generic approach to covariance function estimation using ARMA-models
6- Evaluation of VLBI observations with sensitivity and robustness analyses
7- Variance reduction of sequential Monte Carlo approach for GNSS phase bias estimation
8- Automatic calibration of process noise matrix and measurement noise covariance for multi-GNSS precise point positioning
9- Elementary error model applied to terrestrial laser scanning measurements: Study case arch Dam Kops
10- On estimating the hurst parameter from least-squares residuals. Case study: Correlated terrestrial laser scanner range noiseNuméro de notice : 28459 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-982-9 En ligne : http://dx.doi.org/10.3390/books978-3-03943-982-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99044 Large-scale stochastic flood hazard analysis applied to the Po River / A. Curran in Natural Hazards, vol 104 n° 3 (December 2020)
[article]
Titre : Large-scale stochastic flood hazard analysis applied to the Po River Type de document : Article/Communication Auteurs : A. Curran, Auteur ; Karin De Bruijn, Auteur ; Alessio Domeneghetti, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2027 – 2049 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse des risques
[Termes IGN] digue
[Termes IGN] inondation
[Termes IGN] modèle hydrographique
[Termes IGN] modèle stochastique
[Termes IGN] Pô (plaine)
[Termes IGN] prévention des risques
[Termes IGN] probabilité
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Reliable hazard analysis is crucial in the flood risk management of river basins. For the floodplains of large, developed rivers, flood hazard analysis often needs to account for the complex hydrology of multiple tributaries and the potential failure of dikes. Estimating this hazard using deterministic methods ignores two major aspects of large-scale risk analysis: the spatial–temporal variability of extreme events caused by tributaries, and the uncertainty of dike breach development. Innovative stochastic methods are here developed to account for these uncertainties and are applied to the Po River in Italy. The effects of using these stochastic methods are compared against deterministic equivalents, and the methods are combined to demonstrate applications for an overall stochastic hazard analysis. The results show these uncertainties can impact extreme event water levels by more than 2 m at certain channel locations, and also affect inundation and breaching patterns. The combined hazard analysis allows for probability distributions of flood hazard and dike failure to be developed, which can be used to assess future flood risk management measures. Numéro de notice : A2020-735 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04260-w Date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1007/s11069-020-04260-w Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96350
in Natural Hazards > vol 104 n° 3 (December 2020) . - pp 2027 – 2049[article]A fractal projection and Markovian segmentation-based approach for multimodal change detection / Max Mignotte in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : A fractal projection and Markovian segmentation-based approach for multimodal change detection Type de document : Article/Communication Auteurs : Max Mignotte, Auteur Année de publication : 2020 Article en page(s) : pp 8046 - 8058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification non dirigée
[Termes IGN] décomposition d'image
[Termes IGN] détection de changement
[Termes IGN] estimation bayesienne
[Termes IGN] géométrie fractale
[Termes IGN] image satellite
[Termes IGN] projection
[Termes IGN] segmentation d'imageRésumé : (auteur) Change detection in heterogeneous bitemporal satellite images has become an emerging, important, and challenging research topic in remote sensing for rapid damage assessment. In this article, we explore a new parametric mapping strategy based on a modified geometric fractal decomposition and a contractive mapping approach allowing us to project the before image on any after imaging modality type. This projection exploits the fact that any satellite image data can be approximatively encoded in terms of spatial self-similarities at different scales and this property remains quite invariant to a given imaging modality type. Once the projection is performed and that a pixelwise difference map between the two images (presented in the same imaging modality) is then binarized in the unsupervised Bayesian framework. At this stage, we will test several parameter estimation procedures combined with several segmentation strategies based on different Bayesian cost functions. The experiments for change detection, with real images showing different multimodalities and changed events, indicate that this new fractal-based projection method, which is entirely based on a series of structural and spatial information, is an interesting alternative to classical regression-based projection methods (based only on luminance transformation). Besides, the experiments also show that the difference map, resulting in this novel projection strategy, is also particularly amenable for an unsupervised Markovian binarization approach. Numéro de notice : A2020-682 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2986239 Date de publication en ligne : 30/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2986239 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96207
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8046 - 8058[article]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])
[article]
Titre : Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea Type de document : Article/Communication Auteurs : Sunmin Lee, Auteur ; Moung-Jin Lee, Auteur ; Hyung-Sup Jung, Auteur ; Saro Lee, Auteur Année de publication : 2020 Article en page(s) : pp 1665 - 1679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] carte de la végétation
[Termes IGN] carte forestière
[Termes IGN] carte topographique
[Termes IGN] cartographie des risques
[Termes IGN] catastrophe naturelle
[Termes IGN] Corée du sud
[Termes IGN] effondrement de terrain
[Termes IGN] modèle stochastique
[Termes IGN] réseau bayesien
[Termes IGN] système d'information géographique
[Termes IGN] zone urbaineRésumé : (auteur) In recent years, machine learning techniques have been increasingly applied to the assessment of various natural disasters, including landslides and floods. Machine learning techniques can be used to make predictions based on the relationships among events and their influencing factors. In this study, a machine learning approaches were applied based on landslide location data in a geographic information system environment. Topographic maps were used to determine the topographical factors. Additional soil and forest parameters were examined using information obtained from soil and forest maps. A total of 17 factors affecting landslide occurrence were selected and a spatial database was constructed. Naïve Bayes and Bayesian network models were applied to predict landslides based on selected risk factors. The two models showed accuracies of 78.3 and 79.8%, respectively. The results of this study provide a useful foundation for effective strategies to prevent and manage landslides in urban areas. Numéro de notice : A2020-658 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585482 Date de publication en ligne : 16/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96130
in Geocarto international > vol 35 n° 15 [01/11/2020] . - pp 1665 - 1679[article]Soil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])
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Titre : Soil erosion assessment using RUSLE model and its validation by FR probability model Type de document : Article/Communication Auteurs : Amiya Gayen, Auteur ; Sunil Saha, Auteur ; Hamid Reza Pourghasemi, Auteur Année de publication : 2020 Article en page(s) : pp 1750 - 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] érosion
[Termes IGN] érosion hydrique
[Termes IGN] fréquence
[Termes IGN] Inde
[Termes IGN] modèle RUSLE
[Termes IGN] modèle stochastique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] surveillance géologique
[Termes IGN] utilisation du solRésumé : (auteur) The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas. Numéro de notice : A2020-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581272 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96134
in Geocarto international > vol 35 n° 15 [01/11/2020] . - pp 1750 - 1768[article]Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkIntegration of airborne gravimetry data filtering into residual least-squares collocation: example from the 1 cm geoid experiment / Martin Willberg in Journal of geodesy, vol 94 n° 8 (August 2020)PermalinkEffect of spatial correlation on the performances of modernized GPS and Galileo in relative positioning / Noureddine Kheloufi in Geodesy and cartography, vol 46 n° 2 (July 2020)PermalinkStochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty / Thanate Jongrujinan in Journal of applied geodesy, vol 14 n° 3 (July 2020)PermalinkA probabilistic framework for improving reverse geocoding output / Zhengcong Yin in Transactions in GIS, Vol 24 n° 3 (June 2020)PermalinkRegion level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkSelf-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)PermalinkA single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables / Baocheng Zhang in Journal of geodesy, vol 94 n°4 (April 2020)PermalinkA spatio-temporal deformation model for laser scanning point clouds / Corinna Harmening in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkProbabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)Permalink