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Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)
Titre : Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control Titre original : Exploration des algorithmes d'apprentissage par renforcement pour la perception et le controle d'un véhicule autonome par vision Type de document : Thèse/HDR Auteurs : Florence Carton, Auteur ; David Filliat, Directeur de thèse Editeur : Paris : Ecole Nationale Supérieure des Techniques Avancées ENSTA Année de publication : 2021 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l’Institut Polytechnique de Paris, Spécialité : Informatique, Données, IALangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage par renforcement
[Termes IGN] classification dirigée
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal profond
[Termes IGN] robot mobile
[Termes IGN] segmentation sémantique
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Reinforcement learning is an approach to solve a sequential decision making problem. In this formalism, an autonomous agent interacts with an environment and receives rewards based on the decisions it makes. The goal of the agent is to maximize the total amount of rewards it receives. In the reinforcement learning paradigm, the agent learns by trial and error the policy (sequence of actions) that yields the best rewards.In this thesis, we focus on its application to the perception and control of an autonomous vehicle. To stay close to human driving, only the onboard camera is used as input sensor. We focus in particular on end-to-end training, i.e. a direct mapping between information from the environment and the action chosen by the agent. However, training end-to-end reinforcement learning for autonomous driving poses some challenges: the large dimensions of the state and action spaces as well as the instability and weakness of the reinforcement learning signal to train deep neural networks.The approaches we implemented are based on the use of semantic information (image segmentation). In particular, this work explores the joint training of semantic information and navigation.We show that these methods are promising and allow to overcome some limitations. On the one hand, combining segmentation supervised learning with navigation reinforcement learning improves the performance of the agent and its ability to generalize to an unknown environment. On the other hand, it enables to train an agent that will be more robust to unexpected events and able to make decisions limiting the risks.Experiments are conducted in simulation, and numerous comparisons with state of the art methods are made. Note de contenu : 1- Introduction
2- Supervised learning and reinforcement learning background
3- State of the art
4- End-to-end autonomous driving on circuit with reinforcement learning
5- From lane following to robust conditional driving
6- Exploration of methods to reduce overfit
7- ConclusionNuméro de notice : 28325 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Informatique, Données, IA : ENSTA : 2021 DOI : sans En ligne : https://tel.hal.science/tel-03273748/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98363 Extracting event-related information from a corpus regarding soil industrial pollution / Chuanming Dong (2021)
Titre : Extracting event-related information from a corpus regarding soil industrial pollution Type de document : Article/Communication Auteurs : Chuanming Dong , Auteur ; Philippe Gambette, Auteur ; Catherine Dominguès , Auteur Editeur : Setúbal [Portugal] : Science and Technology Publications - Scitepress Année de publication : 2021 Projets : 1-Pas de projet / Conférence : KDIR 2021, 13th International Conference on Knowledge Discovery and Information Retrieval 25/10/2021 27/10/2021 Setubal Portugal OA Proceedings Importance : pp 217 - 224 Note générale : bibliographie
In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, ISBN 978-989-758-533-3Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] corpus
[Termes IGN] découverte de connaissances
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] pollution des sols
[Termes IGN] site pollué
[Termes IGN] traitement du langage naturelRésumé : (auteur) We study the extraction and reorganization of event-related information in texts regarding industrial pollution. The object is to build a memory of polluted sites that gathers the information about industrial events from various databases and corpora. An industrial event is described through several features as the event trigger, the industrial activity, the institution, the pollutant, etc. In order to efficiently collect information from a large corpus, it is necessary to automatize the information extraction process. To this end, we manually annotated a part of a corpus about soil industrial pollution, then we used it to train information extraction models with deep learning methods. The models we trained achieve 0.76 F-score on event feature extraction. We intend to improve the models and then use them on other text resources to enrich the polluted sites memory with extracted information about industrial events. Numéro de notice : C2021-068 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5220/0010656700003064 En ligne : https://dx.doi.org/10.5220/0010656700003064 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99540 Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Extraction of street pole-like objects based on plane filtering from mobile LiDAR data Type de document : Article/Communication Auteurs : Jingming Tu, Auteur ; Jian Yao, Auteur ; Li Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 749 - 768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte routière
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forme caractéristique
[Termes IGN] méthode robuste
[Termes IGN] octree
[Termes IGN] réseau routierRésumé : (auteur) Pole-like objects provide important street infrastructure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66%, 88.81%, and 79.03%, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently. Numéro de notice : A2021-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2993454 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2993454 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96758
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 749 - 768[article]From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données forestières
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] échantillonnage
[Termes IGN] France (administrative)
[Termes IGN] image Sentinel-MSI
[Termes IGN] mise à jour de base de données
[Termes IGN] Populus (genre)
[Termes IGN] série temporelleRésumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 Date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)
Titre : From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction Type de document : Thèse/HDR Auteurs : Audrey Richard, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2021 Note générale : bibliographie
A thesis submitted to attain the degree of Doctor of Sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] démonstration de faisabilité
[Termes IGN] discrétisation spatiale
[Termes IGN] jeu de données localisées
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modélisation sémantique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Pays-Bas
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] Rhénanie du Nord-Wesphalie (Allemagne)
[Termes IGN] semis de points
[Termes IGN] texturage
[Termes IGN] Zurich (Suisse)Résumé : (auteur) Capturing automatically a virtual 3D model of an object or a scene from a collection of images is a useful capability with a wide range of applications, including virtual/augmented reality, heritage preservation, consumer digital entertainment, autonomous robotics, navigation, industrial vision or metrology, and many more. Since the early days of photogrammetry and computer vision, it has been a topic of intensive research but has eluded a general solution for it. 3D modeling requires more than reconstructing a cloud of 3D points from images; it requires a high-fidelity representation whose form is often dependent on individual objects. This thesis guides you in the journey of image-based 3D reconstruction through several advanced methods that aims to push its boundaries, from precise and complete geometry to detailed appearance, using both theory with elegant mathematics and more recent breakthroughs in deep learning. To evaluate these methods, thorough experiments are conducted at scene level (and large-scale) where efficiency is of key importance, and at object level where accuracy, completeness and photorealism can be better appreciated. To show the individual potential of each of these methods, as well as the possible wide coverage in terms of applications, different scenarios are considered and serve as a proof-of-concept. Thereby, the journey starts with large-scale city modeling using aerial photography from the cities of Zürich (Switzerland), Enschede (Netherlands) and Dortmund (Germany), followed by single object completion using the synthetic dataset ShapeNet, that includes objects like cars, benches or planes that can be found in every city, to finish with the embellishment of these digital models via high-resolution texture mapping using a multi-view 3D dataset of real and synthetic objects, like for example statues and fountains that also dress the landscape of cities. Combining them together into an incremental pipeline dedicated to a specific application would require further tailoring but is quite possible. Numéro de notice : 17650 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Sciences : ETH Zurich : 2021 En ligne : http://dx.doi.org/10.3929/ethz-b-000461735 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97892 FuNet: A novel road extraction network with fusion of location data and remote sensing imagery / Kai Zhou in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkGenerative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)PermalinkPermalinkGeometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)PermalinkPermalinkImage matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 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)PermalinkIntégration et analyse de données massives et hétérogènes pour une observation intelligente du territoire / Rodrigue Kafando (2021)PermalinkPermalinkIntelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)PermalinkPermalinkPermalinkLANet: 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)PermalinkLearning-based representations and methods for 3D shape analysis, manipulation and reconstruction / Marie-Julie Rakotosaona (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)PermalinkPermalinkLearning to translate land-cover maps: Several multi-dimensional context-wise solutions / Luc Baudoux (2021)PermalinkLeveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)PermalinkPermalinkMachine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (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)PermalinkMéthodes de partage d'informations visuelles et inertielles pour la localisation et la cartographie simultanées décentralisées multi-robots / Rodolphe Dubois (2021)PermalinkPermalinkMise en place de nouvelles méthodes d’acquisition par lasergrammétrie en milieu difficile et couvert forestier en vue de la construction d’un parc éolien / Jean-Baptiste Myotte-Duquet (2021)PermalinkPermalinkModélisation et raisonnement spatial flou pour l’aide à la localisation de victimes en montagne / Mattia Bunel (2021)PermalinkPermalinkPermalinkPermalinkA new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network / Wang Li in Advances in space research, vol 67 n° 1 (January 2021)PermalinkPermalinkPanoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)PermalinkPermalinkProduction et mise à jour d’un produit BD Forêt V3 par apprentissage profond / Sébastien Giordano (2021)PermalinkRapport d'activité 2020 de l'Institut National de l'Information Géographique et Forestière IGN, 1. 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