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Titre : Structured learning of geospatial data Type de document : Thèse/HDR Auteurs : Loïc Landrieu , Auteur Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2023 Importance : 179 p. Format : 21 x 30 cm Note générale : Bibliographie
Habilitation à Diriger des Recherches délivrée par l'Université Gustave Eiffel, Spécialité "Sciences et Technologies de l'Information Géographique"Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] apprentissage automatique
[Termes IGN] carte agricole
[Termes IGN] graphe
[Termes IGN] lasergrammétrie
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] vision par ordinateurRésumé : (auteur) This manuscript presents an overview of my work in the field of geospatial machine learning, a rapidly growing interdisciplinary field that poses many methodological challenges and has a wide range of impactful applications. Throughout my research, I have focused on developing bespoke approaches that leverage the unique properties of geospatial data to create more efficient, precise, and parsimonious models. This manuscript is divided into four main chapters, each covering a different property of geospatial data structures that can be leveraged algorithmically. The first chapter presents a versatile mathematical framework formalizing the concept of spatial regularity with graphs. We propose an efficient algorithm that tackles a broad family of spatial problems and provides novel convergence guarantees and significant speed-ups compared to generic approaches. The second chapter introduces a deep learning method that extends the idea of exploiting graph regularity to the case of massive 3D point clouds. We simplify the task of large-scale semantic segmentation by formulating it as as a small graph labelling problem. Our compact models reach high precision at a fraction of the computational cost of other approaches. In the third chapter, we present a collection of methods designed to take advantage of the data structure inherited from 3D sensors. By considering the sensors’ structure, we develop powerful networks with state-of-the-art accuracy, latency, and robustness for various applications and data types. The last chapter dives into the real-life challenge of automated satellite time series analysis for crop mapping. Recognizing the difference between such data and standard formats used in computer vision, we propose novel and streamlined architectures that achieve unprecedented precision while remaining efficient and economical in memory and preprocessing. We also introduce the task of panoptic segmentation for satellite time series and an efficient architecture to solve this problem at scale. In summary, this manuscript argues that geospatial problems represent a challenging and impactful venue for evaluating the newest machine learning and vision methods and a fertile source of inspiration for designing novel approaches. Note de contenu : 1- Introduction
2- Exploiting graph regularity
3- Exploiting the spatial regularity of 3D data
4- Exploiting the structure of 3D sensors
5- Exploiting the structure of satellite time series
6- Perspectives
7- Curriculum vitaeNuméro de notice : 24107 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : HDR Note de thèse : HDR: Sciences et Technologies de l’Information Geographique : UGE : 2023 Organisme de stage : LASTIG (IGN) DOI : sans En ligne : https://hal.science/tel-04095452v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103248 Fast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Fast weakly supervised detection of railway-related infrastructures in lidar acquisitions Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Jean-Philippe Riant, Auteur ; Jean-Christophe Michelin , Auteur ; Sofia Costa d’Aguiar, Auteur Année de publication : 2021 Conférence : ISPRS 2021, Commission 2, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Annals Commission 2 Article en page(s) : pp 27 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] réseau ferroviaire
[Termes IGN] segmentationRésumé : (auteur) Railroad environments are peculiar, as they combine dense urban areas, along with rural parts. They also display a very specific spatial organization. In order to monitor a railway network a at country scale, LiDAR sensors can be equipped on a running train, performing a full acquisition of the network. Then most processing steps are manually done. In this paper, we propose to improve performances and production flow by creating a classification of the acquired data. However, there exists no public benchmark, and little work on LiDAR data classification in railroad environments. Thus, we propose a weakly supervised method for the pointwise classification of such data. We show that our method can be improved by using the l0-cut pursuit algorithm and regularize the noisy pointwise classification on the produced segmentation. As production is envisaged in our context, we designed our implementation such that it is computationally efficient. We evaluate our results against a manual classification, and show that our method can reach a FScore of 0.96 with just a few samples of each class. Numéro de notice : A2021-615 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-27-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-27-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97953
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 27 - 34[article]
Titre : Parallel cut pursuit for minimization of the graph total variation Type de document : Article/Communication Auteurs : Hugo Raguet, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2019 Conférence : ICML 2019, Workshop on Learning and Reasoning with Graph-Structured Representations in International Conference on Machine Learning 15/06/2019 15/06/2019 Long Beach Californie - Etats-Unis Open Access Proceedings Importance : 6 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) We present a parallel version of the cut-pursuit algorithm for minimizing functionals involving the graph total variation. We show that the decomposition of the iterate into constant connected components, which is at the center of this method, allows for the seamless parallelization of the otherwise costly graph-cut based refinement stage. We demonstrate experimentally the efficiency of our method in a wide variety of settings, from simple denoising on huge graphs to more complex inverse problems with nondifferentiable penalties. We argue that our approach combines the efficiency of graph-cuts based optimizers with the versatility and ease of parallelization of traditional proximal. Numéro de notice : C2019-051 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.48550/arXiv.1905.02316 Date de publication en ligne : 07/05/2019 En ligne : https://graphreason.github.io/papers/10.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93350 Cut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation / Hugo Raguet (2018)
Titre : Cut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation Type de document : Article/Communication Auteurs : Hugo Raguet, Auteur ; Loïc Landrieu , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2018 Projets : HYEP / Weber, Christiane Conférence : ICML 2018, 35th International Conference on Machine Learning 10/07/2018 15/07/2018 Stockholm Suède Open Access Proceedings Importance : 10 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] graphe
[Termes IGN] régularisationRésumé : (auteur) We present an extension of the cut-pursuit algorithm, introduced by Landrieu & Obozinski (2017), to the graph total-variation regularization of functions with a separable non differentiable part. We propose a modified algorithmic scheme as well as adapted proofs of convergence. We also present a heuristic approach for handling the cases in which the values associated to each vertex of the graph are multidimensional. The performance of our algorithm, which we demonstrate on difficult, ill-conditioned large-scale inverse and learning problems, is such that it may in practice extend the scope of application of the total-variation regularization. Numéro de notice : C2018-020 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans En ligne : http://proceedings.mlr.press/v80/raguet18a/raguet18a.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90482 Documents numériques
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Cut-Pursuit algorithm for ... - pdf éditeurAdobe Acrobat PDF Cut Pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs / Loïc Landrieu in SIAM Journal on Imaging Sciences, vol 10 n° 4 (November 2017)
[article]
Titre : Cut Pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Guillaume Obozinski, Auteur Année de publication : 2017 Projets : 2-Pas d'info accessible - article non ouvert / Weber, Christiane Article en page(s) : pp 1724 - 1766 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme Cut Pursuit
[Termes IGN] graphe
[Termes IGN] pondérationRésumé : (auteur) We propose working set/greedy algorithms to efficiently solve problems penalized, respectively, by the total variation on a general weighted graph and its $\ell_0$ counterpart the total level-set boundary size when the piecewise constant solutions have a small number of distinct level sets; this is typically the case when the total level-set boundary size is small, which is encouraged by these two forms of penalization. Our algorithms exploit this structure by recursively splitting the level sets of a piecewise constant candidate solution using graph cuts. We obtain significant speedups over state-of-the-art algorithms for images that are well approximated with few level sets. Numéro de notice : A2017-891 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1137/17M1113436 Date de publication en ligne : 10/10/2017 En ligne : https://doi.org/10.1137/17M1113436 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91884
in SIAM Journal on Imaging Sciences > vol 10 n° 4 (November 2017) . - pp 1724 - 1766[article]Permalink