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Semantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)
Titre : Semantic aware quality evaluation of 3D building models : Modeling and simulation Titre original : Evaluation de la qualité des modèles 3D de bâtiments Type de document : Thèse/HDR Auteurs : Oussama Ennafii , Auteur ; Clément Mallet , Directeur de thèse ; Florent Lafarge, Directeur de thèse Editeur : Champs/Marne : Université Paris-Est Année de publication : 2019 Importance : 238 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy delivered by Université Paris-Est, Speciality Geographical Information Sciences and Technologies
Thèse récompensée par le prix 2020 EuroSDR PhD Award.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'erreur
[Termes IGN] généralisation
[Termes IGN] image à très haute résolution
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] modélisation du bâti
[Termes IGN] scène urbaine
[Termes IGN] taxinomieIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The automatic generation of 3D building models from geospatial data is now a standard procedure. An abundant literature covers the last two decades and several softwares are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts, and is highly time-consuming with approximately two hours/km² per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and modular taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest or Support Vector Machine classifiers. Richer features, relying on graph kernels as well as Scattering Networks, were proposed to better take into consideration structure. Both multi-class and multi-label cases are studied: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3,000 buildings. 80%-99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It shows the necessity of multi-modal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Note de contenu : 1- Introduction
2- State of the art
3- Semantic evaluation of 3D models
4- A learning approach for quality evaluation
5- Assessing the learned approach
6- Computing a better representation
7- Assessing the advanced features
8- ConclusionNuméro de notice : 25860 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Sciences et Technologies de l'Information Géographique : Paris-Est, 2019 Organisme de stage : Lastig (IGN) nature-HAL : Thèse DOI : sans En ligne : https://hal.science/tel-02879809 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95395 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 25860-02 THESE Livre Centre de documentation Thèses Disponible 25860-01 THESE Livre Centre de documentation Thèses Disponible 25860-03 THESE Livre Centre de documentation Thèses Disponible Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)
[article]
Titre : Simultaneous chain-forming and generalization of road networks Type de document : Article/Communication Auteurs : Susanne Wenzel, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] analyse de groupement
[Termes IGN] Autriche
[Termes IGN] axe médian
[Termes IGN] classification bayesienne
[Termes IGN] extraction du réseau routier
[Termes IGN] itération
[Termes IGN] mise à jour automatique
[Termes IGN] Munich
[Termes IGN] objet géographique linéaire
[Termes IGN] orthoimage
[Termes IGN] polyligne
[Termes IGN] primitive géométrique
[Termes IGN] relation topologique
[Termes IGN] réseau routier
[Termes IGN] segmentation sémantique
[Termes IGN] squelettisation
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Streets are essential entities of urban terrain and their automatic extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological, and semantic aspects. Given a binary image representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. Thereby, we aim at a fusion of raw segments to longer chains which better match to the intuitive perception of what a street is. We propose a two-step approach for simultaneous chain-forming and generalization. First, we obtain an over-segmentation of the raw polylines. Then, a model selection approach is applied to decide whether two neighboring segments should be fused to a new geometric entity. For this purpose, we propose an iterative greedy optimization procedure in order to find a strong minimum of a cost function based on a Bayesian information criterion. Starting at the given initial raw segments, we thus can obtain a set of chains describing long alleys and important roundabouts. Within the procedure, topological attributes, such as junctions and neighborhood structures, are consistently updated, in a way that for the greedy optimization procedure, accuracy, model complexity, and topology are considered simultaneously. The results on two challenging datasets indicate the benefits of the proposed procedure and provide ideas for future work. Numéro de notice : A2019-026 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.19 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.19 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91962
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 19 - 28[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019011 SL Revue Centre de documentation Revues en salle Disponible Software comparison for underwater archaeological photogrammetric applications / Marinos Vlachos (2019)
Titre : Software comparison for underwater archaeological photogrammetric applications Type de document : Article/Communication Auteurs : Marinos Vlachos, Auteur ; Louise Berger, Auteur ; Rose Mathelier, Auteur ; P. Agrafiotis, Auteur ; Dimitrios Skarlatos, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2019 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-2/W15 Projets : 3-projet - voir note / Conférence : CIPA 2019, 27th CIPA International Symposium, Documenting the past for a better future 01/09/2019 05/09/2019 Ávila Espagne OA ISPRS Archives Importance : 7 p. Note générale : bibliographie
The contribution of M. Vlachos, P. Agrafiotis and D. Skarlatos is part of iMARECULTURE project (Advanced VR, iMmersive Serious Games and Augmented REality as Tools to Raise Awareness and Access to European Underwater CULTURal heritagE, Digital Heritage) that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727153.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse comparative
[Termes IGN] données localisées 3D
[Termes IGN] logiciel de photogrammétrie
[Termes IGN] scène sous-marine
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) This paper presents an investigation as to whether and how the selection of the SfM-MVS software affects the 3D reconstruction of submerged archaeological sites. Specifically, Agisoft Photoscan, VisualSFM, SURE, 3D Zephyr and Reality Capture software were used and evaluated according to their performance in 3D reconstruction using specific metrics over the reconstructed underwater scenes. It must be clarified that the scope of this study is not to evaluate specific algorithms or steps that the various software use, but to evaluate the final results and specifically the generated 3D point clouds. To address the above research issues, a dataset from the ancient shipwreck, laying at 45 meters below sea level, is used. The dataset is composed of 19 images having very small camera to object distance (1 meter), and 42 images with higher camera to object distance (3 meters) images. Using a common bundle adjustment for all 61 images, a reference point cloud resulted from the lower dataset is used to compare it with the point clouds of the higher dataset generated using the different photogrammetric packages. Following that, a comparison regarding the number of total points, cloud to cloud distances, surface roughness, surface density and a combined 3D metric was done to evaluate and see which one performed the best. Numéro de notice : C2019-074 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-2-W15-1195-2019 Date de publication en ligne : 26/08/2019 En ligne : https://doi.org/10.5194/isprs-archives-XLII-2-W15-1195-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99700 Spatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data / Nikolaos Sideris (2019)
Titre : Spatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data Type de document : Thèse/HDR Auteurs : Nikolaos Sideris, Auteur ; Georgios Miaoulis, Directeur de thèse ; Djamchid Ghazanfarpour, Directeur de thèse Editeur : Limoges : Université de Limoges Année de publication : 2019 Importance : 136 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Limoges spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] aide à la décision
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multisources
[Termes IGN] géoréférencement
[Termes IGN] géovisualisation
[Termes IGN] image 3D
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modélisation sémantique
[Termes IGN] ontologie
[Termes IGN] planification urbaine
[Termes IGN] système d'information urbain
[Termes IGN] urbanismeIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this thesis we propose an approach to address the preceding challenges availed with machine learning techniques with the random forests classifier as its dominant method in a system that combines, blends and merges various types of data from different sources, encode them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier. The data are also forwarded to alternative classifiers and the results are appraised to confirm the prevalence of the proposed method. The data retrieved stem from a multitude of sources, e.g. open data providers and public organizations dealing with urban planning. Upon their retrieval and inspection at various levels (e.g. import, conversion, geospatial) they are appropriately converted to comply with the rules of the semantic model and the technical specifications of the corresponding subsystems. Geometrical and geographical calculations are performed and semantic information is extracted. Finally, the information from individual earlier stages along with the results from the machine learning techniques and the multicriteria methods are integrated into the system and visualized in a front-end web based environment able to execute and visualize spatial queries, allow the management of three-dimensional georeferenced objects, their retrieval, transformation and visualization, as a decision support system. Note de contenu : Introduction
1- Theorical background and State of the Art
2- Thesis contribution to semantic querying, navigation and spatial decision Making of 3D Urban Scenes using Machine Learning
3- Evaluation discussion et conclusionsNuméro de notice : 25995 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Université de Limoges : 2019 Organisme de stage : XLIM (Limoges) nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02449667/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96808 Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)
[article]
Titre : Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] compensation par faisceaux
[Termes IGN] estimation de pose
[Termes IGN] image captée par drone
[Termes IGN] matrice de rotation
[Termes IGN] orientation relative
[Termes IGN] Ransac (algorithme)
[Termes IGN] recouvrement d'images
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters. We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness. Numéro de notice : A2019-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.009 Date de publication en ligne : 15/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91970
in ISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 19 - 41[article]Réservation
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