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DART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)
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Titre : DART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications Titre original : Modélisation 3D du transfert radiatif avec polarisation pour l'étude des surfaces terrestres par télédétection Type de document : Thèse/HDR Auteurs : Yingjie Wang, Auteur ; Jean-Philippe Gastellu-Etchegorry, Directeur de thèse ; A. Deschamps, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2022 Importance : 248 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, spécialité Surfaces et interfaces continentales, hydrologieLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de transfert radiatif
[Termes IGN] modélisation 3D
[Termes IGN] polarisation
[Termes IGN] radianceIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Accurate understanding of the land surface functioning, such as the energy budget, carbon and water cycles, and ecosystem dynamics, is essential to better interpret, predict and mitigate the impact of the expected global changes. It thus requires observing our planet at different spatial and temporal scales that only the remote sensing (RS) can achieve because of its ability to provides systematic and synoptic radiometric observations. These observations can be transformed to surface parameters (e.g., temperature, vegetation biomass, etc.) used as input in process models (e.g., evapotranspiration) or be assimilated in the latter. Understanding the radiation interactions in the land surface and atmosphere is essential in two aspects: interpret RS signals as information about the observed land surfaces, and model the processes of functioning of land surfaces where the radiation participates. This explains the development of radiative transfer models (RTMs) that simulate the radiative budget and RS observations. The initial 3D RTMs in the 1980s simulated basic radiation mechanisms in very schematic representations of land surfaces (e.g., turbid medium, geometric primitive). Since then, their accuracy and performance have been greatly improved to address the increasing need of accurate information about land surfaces as well as the advances of RS instruments. So far, two types of improvements are still needed: 1. More accurate and efficient radiative transfer (RT) modelling (e.g., polarization, specular reflection, atmospheric scattering and emission, etc.) 2. Representation of land surfaces at different realism degrees and spatial scales. DART is one of the most accurate and comprehensive 3D RTMs (dart.omp.eu). It simulates the radiative budget and RS observations of urban and natural landscapes, with topography and atmosphere, from the ultraviolet to the thermal infrared domains. Its initial version, DART-FT, in 1992, used the discrete ordinates method to iteratively track the radiation along finite number of discrete directions in voxelized representations of the landscapes. It has been validated with other RTMs, and also RS and field measurements. However, it cannot simulate RS observations with the presently needed precision because of its voxelized representation of landscapes, and absence of some physical mechanisms (e.g., polarization). During this thesis, in collaboration with the DART team, I developed in DART a new Monte Carlo vector RT mode called DART-Lux that takes full advantage of the latest advances in RT modelling, especially in computer graphics. The central idea is to transfer the radiation transfer problem as a multi-dimensional integral problem and solve it with the Monte Carlo method that is considerably efficient and accurate in computing multi-dimensional integral such as the complex mechanisms (e.g., polarization) in realistic representations of 3D landscapes. For that, I implemented the bidirectional path tracing algorithm that generates a group of "source-sensor" paths by connecting two sub-paths, one is generated starting from the light source and another one is generated starting from the sensor. Then, the contribution of these paths to the integral is estimated by the multiple importance sampling. This method allows to accurately and efficiently simulate polarimetric RS observations of kilometre-scale realistic landscapes coupled with plane-parallel atmosphere, with consideration of the anisotropic scattering, the thermal emission, and the solar induced fluorescence. Compared to DART-FT, DART-Lux improves the computer efficiency (i.e., computer time and memory) usually by a factor of more than 100 for large-scale and complex landscapes. It provides new perspectives for studying the land surface functioning and also for preparing Earth observation satellite missions such as the missions TRISHNA (CNES and ISRO), LSTM and next generation Sentinel-2 (ESA), and CHANGE (NASA). Note de contenu : General introduction
1- Radiometry and radiative transfer
2- Numerical models for radiative transfer
3- DART-Lux: theory and implementation
4- Modelling of atmospheric effects
5- Modelling of polarization
Conclusion and perspectivesNuméro de notice : 24106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Organisme de stage : CESBIO DOI : sans En ligne : https://www.theses.fr/2022TOU30173 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103060
Titre : Deep learning based 3D reconstruction: supervision and representation Type de document : Thèse/HDR Auteurs : François Darmon, Auteur ; Pascal Monasse, Directeur de thèse ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 115 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] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] carte de profondeur
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction
[Termes IGN] géométrie épipolaire
[Termes IGN] maillage
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'intérêt
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motion
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) 3D reconstruction is a long standing problem in computer vision. Yet, state-of-the-art methods still struggle when the images used have large illumination changes, many occlusions or limited textures. Deep Learning holds promises of improving 3D reconstruction in such setups, but classical methods still produce the best results. In this thesis we analyse the specificity of deep learning applied to multiview 3D reconstruction and introduce new deep learning based methods.The first contribution of this thesis is an analysis of the possible supervision for training Deep Learning models for sparse image matching. We introduce a two-step algorithm that first computes low resolution matches using deep learning and then matches classical local features inside the matches regions. We analyze several levels of supervision and show that our new epipolar supervision leads to the best results.The second contribution is also a study of supervision for Deep Learning but applied to another scenario: calibrated 3D reconstruction in the wild. We show that existing unsupervised methods do not work on such data and we introduce a new training technique that solves this issue. We then exhaustively compare unsupervised approach and supervised approaches with different network architectures and training data.Finally, our third contribution is about data representation. Neural implicit representation were recently used for image rendering. We adapt this representation to the multiview reconstruction problem and we introduce a new method that, similar to classical 3D reconstruction techniques, optimizes photo-consistency between projections of multiple images. Our approach outperforms state-of-the-art by a large margin. Note de contenu : 1- Introduction
2- Background
3- Deep learning for guiding keypoint matching
4- Deep Learning based Multi-View Stereo in the wild
5- Multi-view reconstruction with implicit surfaces and patch warping
6- ConclusionNuméro de notice : 24085 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Ponts ParisTech : 2022 Organisme de stage : Laboratoire d'Informatique Gaspard-Monge LIGM DOI : sans En ligne : https://www.theses.fr/2022ENPC0024 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102473 A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces / Hassan Noureddine (2022)
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Titre : A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces Type de document : Thèse/HDR Auteurs : Hassan Noureddine, Auteur ; Christophe Claramunt, Directeur de thèse ; Cyril Ray, Directeur de thèse Editeur : Brest : Université de Bretagne Occidentale Année de publication : 2022 Importance : 113 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Bretagne Occidentale, spécialité GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche hiérarchique
[Termes IGN] détection d'événement
[Termes IGN] données spatiotemporelles
[Termes IGN] espace intérieur
[Termes IGN] espace urbain
[Termes IGN] mobilité humaine
[Termes IGN] modèle sémantique de données
[Termes IGN] modélisation 3D
[Termes IGN] ville intelligenteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The interest in exploiting crowd-sourced information has recently emerged as it can bring many valuable benefits for many application domains. This is particularly the case for realtime mobile crowd-sourcing data often available in indoor and outdoor environments. Such data offers many opportunities for analysing human mobility, especially when associated with multidimensional contextual information. Considering the diversity of multi-environment spaces and where mobility occurs, raises several data modelling, management and processing research challenges.When associated with multiple contextual information, indoor and outdoor mobility analysis stresses the need for appropriate and flexible data abstractions at the modelling level to represent the spatial, temporal and semantic data that arise in a smart city environment. While recent approaches often considered this issue using the common stops and moves model, this does not completely cover the multi-dimensional contextual information that arises in real-time on humans navigating through indoor and outdoor spaces. It also increases the need for computing systems and data architectures to process spatio-temporal data in a timely manner when searching for complex mobility events of interest. Despite the ability to represent spatio-temporal events, such systems require well-defined and flexible data manipulation languages to support abstraction and composition mechanisms for analysing urban mobilities.This thesis aims to provide the necessary constructs for analysing mobile crowd-sensed information that arises in indoor and outdoor spaces. In order to better understand urban mobility data in batch and real-time, we consider a broad range of contextual information that can be associated with mobility data. We introduce an indoor and outdoor spatial data model represented as a multi-layered graph and constructed with crowd-sourced trajectory data. The novelty of the approach lies in the fact that it provides a homogeneous and flexible hierarchical indoor and outdoor spatial model that can be associated with crowd-sensed trajectory data on the fly. Our modelling approach defines generic and flexible semantic trajectories considering multiple collaborative data semantics at different granularities and where trajectory segmentation relies on evolving semantic values. This thesis develops a modelling framework for complex events applied to our indoor and outdoor semantic trajectory model based on a formallanguage that establishes the required operations for the composition of the events. We have implemented data pipelines to examine our approach’s efficiency. The whole approach is experimented and applied to participatory data from a real case study to show its suitability, scalability and performance. Note de contenu : 1- Introduction
2- Related work
3- Semantic trajectory data model For indoor and outdoor spaces
4- Composite event extraction from stream of semantic trajectories
5- ConclusionNuméro de notice : 24080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Brest : 2022 Organisme de stage : Institut de Recherche de l’Ecole Navale DOI : sans En ligne : https://theses.hal.science/tel-03888591 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102293 Historical Vltava River valley–various historical sources within web mapping environment / Jiří Krejčí in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
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Titre : Historical Vltava River valley–various historical sources within web mapping environment Type de document : Article/Communication Auteurs : Jiří Krejčí, Auteur ; Jiří Cajthaml, Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] ArcGIS
[Termes IGN] carte ancienne
[Termes IGN] changement d'utilisation du sol
[Termes IGN] données anciennes
[Termes IGN] géoréférencement
[Termes IGN] modélisation 3D
[Termes IGN] point d'appui
[Termes IGN] République Tchèque
[Termes IGN] rivière
[Termes IGN] système d'information historique
[Termes IGN] vectorisation
[Termes IGN] web mappingRésumé : (auteur) The article deals with a comprehensive information system of the historic Vltava River valley. This system contains a number of resources, which are described. For old maps, which are the basis of the whole system, their georeferencing and potential problems in creating seamless mosaics are described. Other sources of data include old photographs, which are localized and stored in the system, along with the definition point of the place from which they were probably taken. The vectorization of data is described, not only for area features used for the analysis of land-use changes, but also for the vectorization of contours. These were vectorized from old maps and are substantial for the creation of historic DEM. Vectorized footprints of buildings and vectors of other functional areas subsequently serve as a basis for the procedural modeling of the virtual 3D landscape. The creation of such a complex and broad information system cannot be described in one article. The aim of this text is to draw attention to a possible approach to the presentation and visualization of the historic landscape, along with links to important documents. Numéro de notice : A2022-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010035 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/ijgi11010035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99380
in ISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 35[article]Levé et numérisation du château de Lichtenberg en vue d’une proposition de visite virtuelle du site à des périodes remarquables / Maxime Rocha (2022)
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Titre : Levé et numérisation du château de Lichtenberg en vue d’une proposition de visite virtuelle du site à des périodes remarquables Type de document : Mémoire Auteurs : Maxime Rocha, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2022 Importance : 66 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de soutenance de Diplôme d’Ingénieur INSALangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Alsace (France administrative)
[Termes IGN] château
[Termes IGN] lasergrammétrie
[Termes IGN] maillage
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] patrimoine culturel
[Termes IGN] restitution
[Termes IGN] semis de points
[Termes IGN] texturage
[Termes IGN] visite virtuelleIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (auteur) Le château de Lichtenberg se situe à environ 60 km au nord de Strasbourg dans la ville éponyme. Il s’agit d’un château datant du 13ème siècle. L’objectif de ce projet est de faire connaître ce site à un plus large public. Pour cela, nous allons utiliser le nuage de points obtenus grâce aux acquisitions effectuées ainsi que des modèles 3D construits sur la base de différents documents d’archives tels que des photographies ou encore des plans afin de créer une vidéo de visite virtuelle. Cette vidéo parcourra l’ensemble du site de nos jours jusqu’à sa création au 13ème siècle et sera diffusée dans l’auditorium présent sur le site. Note de contenu : Introduction
1- Etat de l'art
2- Acquisition et traitement des données du château de Lichtenberg
3- Modélisation 3D des bâtiments
conclusionNuméro de notice : 24094 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur INSAS En ligne : http://eprints2.insa-strasbourg.fr/4882/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102565 Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)
PermalinkPermalinkSpatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)
PermalinkToward enhanced ecodesign of urban project thanks to City Information Modelling / Alexandre Mielniczek (2022)
PermalinkAccuracy assessment of RTK-GNSS equipped UAV conducted as-built surveys for construction site modelling / Sander Varbla in Survey review, Vol 53 n° 381 (November 2021)
PermalinkIonospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)
PermalinkUtilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)
PermalinkEvaluating urban intensity through a city information model - intermediate results from an action research project / Adeline Deprêtre in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol VIII-4/W2-2021 ([07/10/2021])
PermalinkLeast squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
PermalinkA novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)
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