Descripteur
Termes IGN > mathématiques > statistique mathématique
statistique mathématique
Commentaire :
>>
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. |
Documents disponibles dans cette catégorie (6658)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Geometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)
![]()
Titre : Geometric and semantic joint approach for the reconstruction of digital models of buildings Type de document : Thèse/HDR Auteurs : Pierre-Alain Langlois, Auteur ; Renaud Marlet, Directeur de thèse ; Alexandre Boulch, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2021 Importance : 131 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat de l’Ecole des Ponts ParisTech, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] reconnaissance de surface
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] texture d'imageIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The advent of Building Information Models (BIM) in the field of construction and city management revolutionizes the way we design, build, operate and maintain our buildings. BIM models not only include the geometric aspect of the buildings but also semantic information which identifies its logical components (walls, slabs, windows, doors, etc..). While this information is fairly reasonable to create during the building design, only 1% of the building stock is renewed each year. There is therefore an increasing need for automated methods to generate BIM models on existing buildings from sensors such as simple RGB cameras or more advanced Lidar sensors which directly provide a point cloud.In this context, the goal of this thesis is to develop approaches for BIM reconstruction, including both geometric reconstruction and semantic analysis.These tasks have been explored, but an important research effort is conducted to make them more robust to the variety of use cases found in practice.3D reconstruction is usually operated based on direct 3D acquisitions such as Lidars or using photogrammetry, i.e., using pictures to triangulate key point locations and reconstruct the surface afterward. In the context of buildings, the later case is usually limited by the presence of textureless areas which make it hard for the algorithms to find key points and to triangulate them. Moreover, some parts of the buildings might be missing from the input data because of occlusions or omission from the acquisition operator.Regarding semantics in point clouds, important ambiguities exist between semantic classes: the discontinuity between a wall and a door can be hard to distinguish; a slab, a roof and a ceiling sometimes need additional context to be disentangled.In this thesis, we present three technical contributions to address these issues.First, for 3D reconstruction of building scenes, we propose the first method to reconstruct piecewise-planar scenes from images using line segments as primitives. While wide textureless areas exist in indoor scenes (e.g., walls), making it particularly difficult to detect key points, lines are often more visible and easier to detect (e.g., change of illumination at the intersection of two walls) and therefore should be used to ensure robustness of image-based reconstruction approaches. We leverage the presence of these line segments and the possibility to detect and triangulate them. This makes the method robust to textureless surfaces, and we show that we can reconstruct scenes on which point-based methods fail.The second contribution is more theoretical and addresses the problem of mesh reconstruction from multiple calibrated images of low resolution. In this context, traditional methods completely fail and directly learning priors on a large scale dataset of 3D shapes allows us to still perform reconstruction. More specifically, our method uses the learned priors to provide an initial rough shape which is further refined by incorporating geometric constraints. Our method directly outputs a mesh and competes with state of the art methods which can only output a noisy point cloud.Finally, the third technical contribution is VASAD, a dataset for volumetric and semantic reconstruction, which we created from raw BIM models available online. It is the first large scale dataset (62000m²) to offer both geometric and semantic annotation at point and mesh level. With this dataset, we propose two methods to jointly reconstruct both geometry and semantics from a point cloud and we show that the proposed dataset is challenging enough to stimulate research. Note de contenu : 1. Introduction
1.1 Motivation
1.2 Approach
1.3 Contributions
1.4 Organization of the dissertation
SURFACE RECONSTRUCTION FROM 3D LINE SEGMENTS
2. Introduction
2.1 Reconstructing textureless surfaces
2.2 Related Work
3. Method
3.1 Line extraction
3.2 Plane detection from 3D line segments
3.3 Surface reconstruction
4. Results
4.1 Datasets
4.2 Observations on the input data
4.3 Qualitative evaluation of reconstructions
4.4 Quantitative evaluation of reconstructions
4.5 Ablation study
4.6 Limitations and perspectives
4.7 Conclusion
3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING
5. Introduction
5.1 Learning 3D reconstruction
5.2 Related work
6. Method
6.1 Learning a Multi-View Parameterized Surface Mapping
6.2 Design choices
7. Results
7.1 Dataset
7.2 Evaluation criteria
7.3 Experimental results
7.4 Ablation study
7.5 Discussion and limitations
7.6 Conclusion
VASAD: A VOLUME AND SEMANTIC DATASET FOR BUILDING RECONSTRUCTION FROM POINT CLOUDS
8. Introduction
8.1 3D Reconstruction for buildings
8.2 Related work
9. DATASET
9.1 Building information models
9.2 Presentation of the dataset
9.3 3D representation
9.4 Point cloud simulation
9.5 Train/test split
10. Method
10.1 Reconstruction approaches
10.2 PVSRNet
10.3 Semantic Convolutional Occupancy Network
10.4 Data preparation
11. RESULTS
11.1 Metrics
11.2 Surface reconstruction
11.3 Semantic segmentation
11.4 Discussion
11.5 Conclusion
EPILOGUE
12. Conclusion
12.1 Looking back
12.2 Looking aheadNuméro de notice : 26822 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Champs-Sur-Marne : 2021 Organisme de stage : Laboratoire d'Informatique Gaspard Monge LIGM nature-HAL : Thèse DOI : sans Date de publication en ligne : 11/04/2022 En ligne : https://tel.hal.science/tel-03637158/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100564 Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)
![]()
Titre : Geometric computer vision: omnidirectional visual and remotely sensed data analysis Type de document : Thèse/HDR Auteurs : Pouria Babahajiani, Auteur ; Moncef Gabbouj, Directeur de thèse Editeur : Tampere [Finlande] : Tampere University Année de publication : 2021 Importance : 147 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-952-03-1979-3 Note générale : bibliographie
Accademic Dissertation, Tampere University, Faculty of Information Technology and Communication Sciences FinlandLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effet de profondeur cinétique
[Termes IGN] espace public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image panoramique
[Termes IGN] image Streetview
[Termes IGN] image terrestre
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle sémantique de données
[Termes IGN] réalité virtuelle
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] vision par ordinateur
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Information about the surrounding environment perceived by the human eye is one of the most important cues enabled by sight. The scientific community has put a great effort throughout time to develop methods for scene acquisition and scene understanding using computer vision techniques. The goal of this thesis is to study geometry in computer vision and its applications. In computer vision, geometry describes the topological structure of the environment. Specifically, it concerns measures such as shape, volume, depth, pose, disparity, motion, and optical flow, all of which are essential cues in scene acquisition and understanding.
This thesis focuses on two primary objectives. The first is to assess the feasibility of creating semantic models of urban areas and public spaces using geometrical features coming from LiDAR sensors. The second objective is to develop a practical Virtual Reality (VR) video representation that supports 6-Degrees-of-Freedom (DoF) head motion parallax using geometric computer vision and machine learning. The thesis’s first contribution is the proposal of semantic segmentation of the 3D LiDAR point cloud and its applications. The ever-growing demand for reliable mapping data, especially in urban environments, has motivated mobile mapping systems’ development. These systems acquire high precision data and, in particular 3D LiDAR point clouds and optical images. A large amount of data and their diversity make data processing a complex task. A complete urban map data processing pipeline has been developed, which annotates 3D LiDAR points with semantic labels. The proposed method is made efficient by combining fast rule-based processing for building and street surface segmentation and super-voxel-based feature extraction and classification for the remaining map elements (cars, pedestrians, trees, and traffic signs). Based on the experiments, the rule-based processing stage provides substantial improvement not only in computational time but also in classification accuracy. Furthermore, two back ends are developed for semantically labeled data that exemplify two important applications: (1) 3D high definition urban map that reconstructs a realistic 3D model using input labeled point cloud, and (2) semantic segmentation of 2D street view images. The second contribution of the thesis is the development of a practical, fast, and robust method to create high-resolution Depth-Augmented Stereo Panoramas (DASP) from a 360-degree VR camera. A novel and complete optical flow-based pipeline is developed, which provides stereo 360-views of a real-world scene with DASP. The system consists of a texture and depth panorama for each eye. A bi-directional flow estimation network is explicitly designed for stitching and stereo depth estimation, which yields state-of-the-art results with a limited run-time budget. The proposed architecture explicitly leverages geometry by getting both optical flow ground-truths. Building architectures that use this knowledge simplifies the learning problem. Moreover, a 6-DoF testbed for immersive content quality assessment is proposed. Modern machine learning techniques have been used to design the proposed architectures addressing many core computer vision problems by exploiting the enriched information coming from 3D scene structures. The architectures proposed in this thesis are practical systems that impact today’s technologies, including autonomous vehicles, virtual reality, augmented reality, robots, and smart-city infrastructures.Note de contenu : 1- Introduction
2- Geometry in Computer Vision
3- Contributions
4- ConclusionNuméro de notice : 28323 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Computing and Electrical Engineering : Tempere, Finland : 2021 DOI : sans En ligne : https://trepo.tuni.fi/handle/10024/131379 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98342
Titre : Geospatial analysis of the spreading of COVID-19 In the United States Type de document : Mémoire Auteurs : Otto Heimonen, Auteur Editeur : Tampere [Finlande] : Tampere University Année de publication : 2021 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Master’s Degree Programme in Computational Big Data AnalyticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] épidémie
[Termes IGN] estimation bayesienne
[Termes IGN] Etats-Unis
[Termes IGN] maladie infectieuse
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de simulationRésumé : (auteur) The COVID-19 pandemic has been a big threat to public health and there is an increasing need for efficient modelling of pathogens, predicting the daily infection rates to reduce the spread of COVID-19.
The Moran’s and Geary’s statistics showed significant spatial autocorrelation in the infection counts for the
US COVID-19 data. Spatial regression using the simultaneous autoregression (SAR) and conditional autoregression (CAR) models indicate clear association between the confirmed cases and the number of population and the population density in both national county and state specific analyses. The SAR model provided a better model fit with the low AIC value, leaving no significant autocorrelation for the residuals. The approximate Bayesian computation (ABC) methods were used to provide a flexible posterior distribution of the infection rate for COVID-19 based on the first 100 days of the pandemic. Three different simulation methods such as ABC-Rejection, ABC-Markov Chain Monte Carlo (MCMC) and ABC-Sequential Monte Carlo (SMC) were employed and compared. These algorithms seem to give reasonable posterior estimates for the average daily infections when the likelihood calculations for the spread of a harmful pathogen become complex, or intractable entirely. The posterior distributions of ABC-MCMC and ABC-SMC provided plausible estimations covering all of the observed infection rates at different time points.Note de contenu : 1- Introduction
2- Methods
3- Empirical data analysis
4- DiscussionNuméro de notice : 28455 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Mémoire masters divers DOI : sans En ligne : https://trepo.tuni.fi/handle/10024/134567 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99025 GPS + Galileo + QZSS + BDS tightly combined single-epoch single-frequency RTK positioning / Shaolin Zhu in Survey review, vol 53 n°376 (January 2021)
![]()
[article]
Titre : GPS + Galileo + QZSS + BDS tightly combined single-epoch single-frequency RTK positioning Type de document : Article/Communication Auteurs : Shaolin Zhu, Auteur ; Dongjie Yue, Auteur ; Jian Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 16 - 26 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] données BeiDou
[Termes IGN] données Galileo
[Termes IGN] données GPS
[Termes IGN] modèle stochastique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] qualité du signal
[Termes IGN] Quasi-Zenith Satellite System
[Termes IGN] récepteur monofréquence
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) The multi-GNSS fusion makes positioning more reliable and accurate. Considering the signal difference of different systems, GPS + Galileo + QZSS + BDS tightly combined double-difference model (TCDDM), including function and stochastic model, is proposed. The proposed model fully utilizes the overlapping frequency signals of various systems, and thus to enhance positioning model when DISBs are known beforehand. The observations of 3 ultra-short (1~10 m) and 3 short (4~10 km) baselines were processed by self-programming software, and the single-epoch single-frequency RTK performance using different system-combined models was evaluated by ambiguity-fixed correctness rate (ACR) and positioning accuracy. It demonstrated that three- and four-system TCDDM were superior to their corresponding loosely combined double-difference model (LCDDM) for ACR and positioning accuracy especially at high cut-off elevation. Moreover, four-system TCDDM had the best RTK performance obtaining average ACRs of 100% and 97.6% even at 25° cut-off elevation for ultra-short and short baseline, respectively. Numéro de notice : A2021-047 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1681681 Date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1080/00396265.2019.1681681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96782
in Survey review > vol 53 n°376 (January 2021) . - pp 16 - 26[article]
Titre : Harmonized Landsat Sentinel-2 (HLS) Type de document : Mémoire Auteurs : Célestin Huet, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2021 Importance : 41 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] correction atmosphérique
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] harmonisation des données
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TIRS
[Termes IGN] image Sentinel-MSI
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] série temporelle
[Termes IGN] superposition d'imagesIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Depuis quelques années, en télédétection, de plus en plus d’études utilisent les séries temporelles. Pour les satellites d’observation de la Terre comme Landsat 8 ou Sentinel-2, le temps de revisite moyen est de 4,5 jours. Si l’on parvient à modifier les images de ces deux constellations pour considérer qu’elles viennent du même capteur, alors le temps de revisite moyen descend à 2,9 jours. Cela permet une meilleure précision dans les études et d’être moins sensible à la présence de nuages. Actuellement, des recherches sont faites pour harmoniser les images Sentinel-2 et Landsat 8, afin qu’elles puissent constituer un seul et même jeu de données avec une meilleure résolution temporelle. L’objectif de ce stage est d’implémenter l’algorithme Harmonized Landsat Sentinel-2 (HLS) décrit dans "The Harmonized Landsat and Sentinel-2 surface reflectance dataset" (Claverie et al., 2018) et d’essayer de l’étendre aux images Landsat 5 et Landsat 7. Toutefois, à cause de certaines informations absentes dans la description et de l’indisponibilité du code de correction atmosphérique pour la collection 2 de Landsat, les résultats ne sont pas aussi bons qu’espérés. Note de contenu : Introduction
1. Le projet Harmonized Landsat Sentinel-2 (HLS)
1.1 Caractéristiques des satellites
1.2 Produits de l’algorithme
1.3 Étapes de l’algorithme
2. Analyse de l’algorithme
2.1 Recherches initiales
2.2 Données initiales
2.3 La correction atmosphérique
2.4 Les masques
2.5 La superposition spatiale et le rééchantillonnage
2.6 La normalisation BRDF
2.7 L’ajustement des bandes
3 Mise en œuvre de l’algorithme
3.1 Sélection d’images tests
3.2 Cas particulier de Landsat 8
3.3 Correction atmosphérique
3.4 Les masques
3.5 Rééchantillonnage
3.6 Normalisation BRDF
3.7 L’algorithme pour les images Landsat 5 et Landsat 7
4. Analyse des résultats
4.1 Conclusion sur la mise en œuvre de l’algorithme HLS
4.2 Comparaison d’images
4.3 Commentaires sur les algorithmes utilisés
ConclusionNuméro de notice : 26605 Affiliation des auteurs : IGN (2020- ) Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Institute of Anthropological and Spatial Studies Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98505 Documents numériques
peut être téléchargé
Harmonized Landsat Sentinel-2 (HLS) - pdf auteurAdobe Acrobat PDFHidden Markov map matching based on trajectory segmentation with heading homogeneity / Ge Cui in Geoinformatica, vol 25 n° 1 (January 2021)
PermalinkHigh accuracy terrestrial positioning based on time delay and carrier phase using wideband radio signals / Han Dun (2021)
PermalinkA hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging / Anno Löcher in Journal of geodesy, vol 95 n° 1 (January 2021)
PermalinkImage matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)
PermalinkImpact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
PermalinkImports massifs de données dans OpenStreetMap : un phénomène en plein essor / Mamadou Bailo Balde (2021)
PermalinkImproving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkInferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)
PermalinkInitialization methods of convolutional neural networks for detection of image manipulations / Ivan Castillo Camacho (2021)
PermalinkIntegrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)
PermalinkIntelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)
PermalinkPermalinkInvestigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest / Marlin Mueller (2021)
PermalinkLANet: Local attention embedding to improve the semantic segmentation of remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
PermalinkPermalinkLearning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)
PermalinkLearning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)
PermalinkPermalinkPermalinkLeveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)
PermalinkPermalinkLocal fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
PermalinkMask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors / Emilio Guirado in Sensors, vol 21 n° 1 (January 2021)
PermalinkPermalinkA method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)
PermalinkMéthodes et outils pour l’analyse spatiale exploratoire en géolinguistique : contributions aux humanités numériques spatialisées / Clément Chagnaud (2021)
PermalinkPermalinkModel based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)
PermalinkModeling multifrequency GPS multipath fading in land vehicle environments / Vicente Carvalho Lima Filho in GPS solutions, vol 25 n° 1 (January 2021)
PermalinkModélisation de l’aire de réception d’une antenne AIS en fonction de données d’altitude et de cartes de prévision de propagation d’ondes VHF / Zackary Vanche (2021)
PermalinkModelling and building of a graph database of multi-source landmarks to help emergency mountain rescuers / Véronique Gendner (2021)
PermalinkMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkPermalinkPermalinkNorway spruce seedlings from an Eastern Baltic provenance show tolerance to simulated drought / Roberts Matisons in Forests, vol 12 n° 1 (January 2021)
PermalinkObject detection using component-graphs and ConvNets with application to astronomical images / Thanh Xuan Nguyen (2021)
PermalinkPermalinkPanoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)
PermalinkPerception de scène par un système multi-capteurs, application à la navigation dans des environnements d'intérieur structuré / Marwa Chakroun (2021)
PermalinkPermalinkPermalinkProbabilistic positioning in mobile phone network and its consequences for the privacy of mobility data / Aleksey Ogulenko in Computers, Environment and Urban Systems, vol 85 (January 2021)
PermalinkProposition d’un référentiel de description et de détection de la végétation dans une agglomération / Mathilde Segaud (2021)
PermalinkQuantification probabiliste des taux de déformation crustale par inversion bayésienne de données GPS / Colin Pagani (2021)
PermalinkRadio base stations and electromagnetic fields: GIS applications and models for identifying possible risk factors and areas exposed. Some exemplifications in Rome / Cristiano Pesaresi in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
Permalink