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Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74 Titre : Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization Type de document : Article/Communication Auteurs : Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 57 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire de Markov [Termes IGN] données lidar [Termes IGN] espace intérieur [Termes IGN] maillage [Termes IGN] programmation linéaire [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] vectorisation Résumé : (Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. Numéro de notice : A2021-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.019 date de publication en ligne : 15/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779 [article]

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Combinatorial optimization applied to VLBI scheduling / A. Corbin in Journal of geodesy, vol 94 n°2 (February 2020)  

Public [article]  inJournal of geodesy > vol 94 n°2 (February 2020) Titre : Combinatorial optimization applied to VLBI scheduling Type de document : Article/Communication Auteurs : A. Corbin, Auteur ; B. Niedermann, Auteur ; Axel Nothnagel, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse combinatoire (maths) [Termes IGN] données VGOS [Termes IGN] interférométrie à très grande base [Termes IGN] positionnement par ITGB [Termes IGN] programmation linéaire [Termes IGN] retard troposphérique zénithal [Termes IGN] station VLBI [Termes IGN] téléscope [Termes IGN] temps universel coordonné Résumé : (auteur) Due to the advent of powerful solvers, today linear programming has seen many applications in production and routing. In this publication, we present mixed-integer linear programming as applied to scheduling geodetic very-long-baseline interferometry (VLBI) observations. The approach uses combinatorial optimization and formulates the scheduling task as a mixed-integer linear program. Within this new method, the schedule is considered as an entity containing all possible observations of an observing session at the same time, leading to a global optimum. In our example, the optimum is found by maximizing the sky coverage score. The sky coverage score is computed by a hierarchical partitioning of the local sky above each telescope into a number of cells. Each cell including at least one observation adds a certain gain to the score. The method is computationally expensive and this publication may be ahead of its time for large networks and large numbers of VLBI observations. However, considering that developments of solvers for combinatorial optimization are progressing rapidly and that computers increase in performance, the usefulness of this approach may come up again in some distant future. Nevertheless, readers may be prompted to look into these optimization methods already today seeing that they are available also in the geodetic literature. The validity of the concept and the applicability of the logic are demonstrated by evaluating test schedules for five 1-h, single-baseline Intensive VLBI sessions. Compared to schedules that were produced with the scheduling software sked, the number of observations per session is increased on average by three observations and the simulated precision of UT1-UTC is improved in four out of five cases (6 μs average improvement in quadrature). Moreover, a simplified and thus much faster version of the mixed-integer linear program has been developed for modern VLBI Global Observing System telescopes. Numéro de notice : A2020-153 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01348-w date de publication en ligne : 29/01/2020 En ligne : https://doi.org/10.1007/s00190-020-01348-w Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94786 [article]

Exact optimization algorithms for the aggregation of spatial data / Johannes Oehrlein (2020)  

Public Titre : Exact optimization algorithms for the aggregation of spatial data Type de document : Thèse/HDR Auteurs : Johannes Oehrlein, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 862 Importance : 184 p. Format : 21 x 30 cm Note générale : bibliographie Dissertation zur Erlangung des GradesDoktor-Ingenieur (Dr.-Ing.) Diese Arbeit ist gleichzeitig als elektronische Dissertationbei der Universitäts-und Landesbibliothek Bonn veröffentlicht Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale [Termes IGN] cycliste [Termes IGN] données localisées [Termes IGN] espace vert [Termes IGN] généralisation automatique de données [Termes IGN] programmation linéaire [Termes IGN] réseau routier [Termes IGN] trajet [Termes IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) The aggregation of spatial data is a recurring problem in geoinformation science. Aggregating data means subsuming multiple pieces of information into a less complex representation. It is pursued for various reasons, like having a less complex data structure to apply further processing algorithms or a simpler visual representation as targeted in map generalization. In this thesis, we identify aggregation problems dealing with spatial data and formalize themas optimization problems. That means we set up a function that is capable of evaluating valid solutions to the considered problem, like a cost function for minimization problems. To each problem introduced, we present an algorithm that finds a valid solution that optimizes this objective function. In general, this superiority with respect to the quality of the solution comes at the cost of computation efficiency, a reason why non-exact approaches like heuristics are widely used for optimization. Nevertheless, the higher quality of solutions yielded by exact approaches is undoubtedly important. On the one hand, “good” solutions are sometimes not sufficient. On the other hand, exact approaches yield solutions that maybe used as benchmarks for the evaluation of non-exact approaches. This kind of application is of particular interest since heuristic approaches, for example, give no guarantee on the quality of solutions found. Furthermore, algorithms that provide exact solutions to optimization problems reveal weak spots of underlying models. A result that does not satisfy the user cannot be excused with a mediocre performance of an applied heuristic. With this motivation, we developed several exact approaches for aggregation problems, which we present in this thesis. Since we deal with spatial data, for all problems considered, the aggregation is based on both geometric and semantic aspects although the focus varies. The first problem we discuss is about visualizing a road network in the context of navigation. Given a fixed location in the network, we aim for a clear representation of the surroundings. For this purpose, we introduce an equivalence relation for destinations in the network based on which we perform the aggregation. We succeed in designing an efficient algorithm that aggregates as many equivalent destinations as possible. Furthermore, we tackle a class of similar and frequently discussed problems concerning the aggregation of areal units into larger, connected regions. Since these problems are NP-complete, i.e. extraordinarily complex, we do not aim for an efficient exact algorithm (which is suspected not to exist) but present a strong improvement to existing exact approaches. In another setup, we present an efficient algorithm for the analysis of urban green-space supply. Performing a hypothetical assignment of citizens to available green spaces, it detects local shortages and patterns in the accessibility of green space within a city. Finally, we introduce and demonstrate a tool for detecting route preferences of cyclists based on a selection of given trajectories. Examining a set of criteria forming suitable candidates, we aggregate them efficiently to the best-fitting derivable criterion. Overall, we present exact approaches to various aggregation problems. In particular, the NP-complete problem we deal with firmly underscores, as expected, the need for heuristic approaches. For applications asking for an immediate solution, it may be reasonable to apply a heuristic approach. This holds in particular due to easy and generally applicable meta-heuristics being available. However, with this thesis, we argue for applying exact approaches if possible. The guaranteed superior quality of solutions speaks for itself. Besides, we give additional examples which show that exact approaches can be applied efficiently as well. Numéro de notice : 17681 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD dissertation : : Rheinische Friedrich-Wilhelms-Universität Bonn : 2020 En ligne : https://nbn-resolving.org/urn:nbn:de:hbz:5-60713 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98023

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds / Sebastian Ochmann in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 251 - 262 Titre : Automatic reconstruction of fully volumetric 3D building models from oriented point clouds Type de document : Article/Communication Auteurs : Sebastian Ochmann, Auteur ; Richard Vock, Auteur ; Reinhard Klein, Auteur Année de publication : 2019 Article en page(s) : pp 251 - 262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] format Industry foudation classes IFC [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] positionnement en intérieur [Termes IGN] programmation linéaire [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points Résumé : (Auteur) We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds. Numéro de notice : A2019-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.017 date de publication en ligne : 30/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92676 [article]

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Excel data analysis / Hector Guerrero (2019)  

Public Titre : Excel data analysis : modeling and simulation Type de document : Guide/Manuel Auteurs : Hector Guerrero, Auteur Mention d'édition : 2ème édition Editeur : Springer Nature Année de publication : 2019 Importance : 358 p. ISBN/ISSN/EAN : 978-3-030-01279-3 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse de données [Termes IGN] données qualitatives [Termes IGN] Excel (logiciel) [Termes IGN] inférence statistique [Termes IGN] méthode de Monte-Carlo [Termes IGN] programmation linéaire Résumé : (Editeur) This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations. Note de contenu : 1. Introduction to Spreadsheet Modeling 2. Presentation of Quantitative Data: Data Visualization 3. Analysis of Quantitative Data 4. Presentation of Qualitative Data—Data Visualization 5. Analysis of Qualitative Data 6. Inferential Statistical Analysis of Data 7. Modeling and Simulation: Part 1 8. Modeling and Simulation: Part 2 9. Solver, Scenarios, and Goal Seek Tools Numéro de notice : 26280 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-01279-3 En ligne : https://doi.org/10.1007/978-3-030-01279-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94931

Un algorithme pour battre le record du SwissTrainChallenge : poser le pied dans chacun des 26 cantons le plus rapidement possible en utilisant uniquement des transports publics / Emmanuel Cledat in XYZ, n° 157 (décembre 2018 - février 2019)

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Object-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)  

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Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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Linking ecosystem services with state-and-transition models to evaluate rangeland management decisions / Sapana Lohani in Global ecology and conservation, vol 8 (October 2016)  

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Recursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

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Gestion de collecte des déchets post-inondation / Oumayma Kaabi (2016)

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Probabilistic multi-person localisation and tracking / Tobias Klinger (2016) 

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Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

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A comparative analysis of traveling salesman solutions from geographic information systems / Kevin M. Curtin in Transactions in GIS, vol 18 n° 2 (April 2014)  

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Attraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

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