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Modeling multifrequency GPS multipath fading in land vehicle environments / Vicente Carvalho Lima Filho in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - 14 p. Titre : Modeling multifrequency GPS multipath fading in land vehicle environments Type de document : Article/Communication Auteurs : Vicente Carvalho Lima Filho, Auteur ; Alison Moraes, Auteur Année de publication : 2021 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] densité de probabilité [Termes descripteurs IGN] interférence [Termes descripteurs IGN] propagation du signal [Termes descripteurs IGN] qualité du signal [Termes descripteurs IGN] signal GPS [Termes descripteurs IGN] simulation de signal [Termes descripteurs IGN] trajet multiple [Termes descripteurs IGN] véhicule Résumé : (auteur) The reliability and performance of GPS receivers depend on the quality of the signal received, which can be largely affected by the interference caused by buildings, trees, and other obstacles. Since obstacles are always present in practical applications, several statistical representations have been developed along the years to measure, predict, and compensate errors induced by interferences. Two of the most used models to characterize GPS signal fading are the Nakagami-m and Rice, but in this work, we present evidence that supports the κ–μ distribution as the best fit to deal with multifrequency GPS multipath channels inside urban, rural, and forest areas. A synthetic signal simulator was developed to create propagation cases involving scattering clusters and specular reflections. Additionally, experimental measurements are presented to confirm the κ–μ distribution as the best distribution to characterize different situations on the available three GPS frequencies. We then present typical values of fading coefficients in L1, L2C, and L5 signals, for cases involving urban canyons, regular urban, rural, and dense vegetation areas. These coefficients can also be used to evaluate the receiver performance under similar cases or may be applied in weights measurement methods for positioning computation improvement. Numéro de notice : A2021-002 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01040-8 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01040-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96080 [article]

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol inconnu ([01/12/2020])  

Public [article]  inInternational Journal of Digital Earth > vol inconnu [01/12/2020] Titre : A data fusion-based framework to integrate multi-source VGI in an authoritative land use database Type de document : Article/Communication Auteurs : Lanfa Liu, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Laurence Jolivet , Auteur ; Arnaud Le Bris , Auteur ; Linda M. See, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] base de données localisées de référence [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] OCS GE [Termes descripteurs IGN] théorie de Dempster-Shafer Résumé : (auteur) Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally. Numéro de notice : A2020-578 Affiliation des auteurs : LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17538947.2020.1842524 date de publication en ligne : 05/11/2020 En ligne : https://doi.org/10.1080/17538947.2020.1842524 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96522 [article]

Large-scale stochastic flood hazard analysis applied to the Po River / A. Curran in Natural Hazards, vol 104 n° 3 (December 2020)  

Public [article]  inNatural Hazards > vol 104 n° 3 (December 2020) . - pp 2027 – 2049 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] Environnement [Termes descripteurs IGN] analyse des risques [Termes descripteurs IGN] digue [Termes descripteurs IGN] inondation [Termes descripteurs IGN] modèle hydrographique [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] Pô (plaine) [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] probabilité [Termes descripteurs IGN] surveillance hydrologique Résumé : (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 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 [article]

Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2361 - 2394 Titre : Semantic trajectory segmentation based on change-point detection and ontology Type de document : Article/Communication Auteurs : Yuan Gao, Auteur ; Longfei Huang, Auteur ; Jun Feng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2361 - 2394 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] information sémantique [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] probabilité [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] trajectoire Résumé : (auteur) Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject’s intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals’ patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied. Numéro de notice : A2020-689 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1798966 date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1798966 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96226 [article]

Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India / Subrata Mondal in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 818 - 856 Titre : Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India Type de document : Article/Communication Auteurs : Subrata Mondal, Auteur ; Sujit Mandal, Auteur Année de publication : 2020 Article en page(s) : pp 818 - 856 Note générale : bibbliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] action anthropique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] lithologie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] théorie de Dempster-Shafer [Termes descripteurs IGN] vulnérabilité Résumé : (auteur) In the present study, data-driven evidential belief function model (belief function) was employed to generate landslides susceptibility index map of Darjeeling Himalaya considering 15 landslide causative factors, which grouped into six categories, i.e. geomorphological factors (elevation, aspect, slope, curvature), lithological factors (geology, soil, lineament density, distance to lineament), hydrologic factors (drainage density, distance to drainage, stream power index, topographic wetted index), triggering factor (rainfall), protective factor (normalized differential vegetation index) and anthropogenic factor (land use and land cover). Total 2079 landslide locations were mapped and randomly divided it into training datasets (70% landslide locations) and validation datasets (30% landslide locations). The resultant susceptibility map was divided into five different susceptibility zones i.e. very low, low, moderate, high and very high which covered 5.60%, 25.65%, 34.47%, 24.67% and 9.61% area respectively of the Darjeeling Himalaya. Receiver operating characteristics curve suggested that 80.20% prediction accuracy of the prepared map whereas frequency ratio plot indicated towards the ideal landslides susceptibility index map. Numéro de notice : A2020-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2018.1544288 date de publication en ligne : 13/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1544288 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95059 [article]

Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)  

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Applied and computational statistics / Sorana D. Bolboacă (2020)  

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On the path to AI / Thomas D. Grant (2020)  

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Multi-temporal image change mining based on evidential conflict reasoning / Fatma Haouas in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning / Qing Xu in Forest ecology and management, vol 434 (28 February 2019)  

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Ensemble methods for pedestrian detection in dense crowds / Jennifer Vandoni (2019)  

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Introduction to Deep Learning / Sandro Skansi (2018)  

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Probability and statistics / Arak M. Mathai (2018)  

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Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game / Dawei Zai in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

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Salient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)  

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