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Terme regroupant photographies et images issues de différents capteurs.
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Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])
[article]
Titre : Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape Type de document : Article/Communication Auteurs : Niva Kiran Verma, Auteur ; David Lamb, Auteur ; Priyakant Sinha, Auteur Année de publication : 2022 Article en page(s) : pp 70 - 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Australie
[Termes IGN] carte de la végétation
[Termes IGN] dépérissement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] précision de la classification
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (auteur) Rapid decline and death of rural Eucalypts trees of all ages and species have been reported in the farmscapes of regional Australia due to various environmental and farming management related factors. The identification of existing farm tree species is important for long term management strategies to provide ecosystem stability in the region. This study explored the feasibility of structural attributes of LiDAR and spectral and spatial characteristics of high resolution remote sensing data to identify and map Eucalyptus tree species. An object based image segmentation and rule-based classification algorithm were developed to delineate tree boundaries and species classification. The integration of two datasets improved the classification accuracy (65%) against their separate classification (52% and 41%, respectively). The identification of tree species will help in getting first-hand information on existing farm trees, which may be used in assessing tree condition in time series related to management practices and complex dieback problem. Numéro de notice : A2022-046 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700555 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99412
in Geocarto international > vol 37 n° 1 [01/01/2022] . - pp 70 - 90[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022011 RAB Revue Centre de documentation En réserve L003 Disponible ALEGORIA: Joint multimodal search and spatial navigation into the geographic iconographic heritage / Florent Geniet (2022)
Titre : ALEGORIA: Joint multimodal search and spatial navigation into the geographic iconographic heritage Type de document : Article/Communication Auteurs : Florent Geniet, Auteur ; Valérie Gouet-Brunet , Auteur ; Mathieu Brédif , Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : MM 2022, 30th ACM International Conference on Multimedia 10/10/2022 14/10/2022 Lisbonne Portugal Proceedings ACM Importance : pp 6982 - 6984 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] géolocalisation
[Termes IGN] image ancienne
[Termes IGN] moteur de recherche
[Termes IGN] photographie aérienne oblique
[Termes IGN] plateforme logicielle
[Termes IGN] visualisation 3DRésumé : (auteur) In this article, we present two online platforms developed for the structuring and valorization of old geographical iconographic collections: a multimodal search engine for their indexing, retrieval and interlinking, and a 3D navigation platform for their visualization in spatial context. In particular, we show how the joint use of these functionalities, guided by geolocation, brings structure and knowledge to the manipulated collections. In the demonstrator, they consist of 54,000 oblique aerial photographs from several French providers (national archives, a museum and a mapping agency). Numéro de notice : C2022-042 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3503161.3547746 Date de publication en ligne : 10/10/2022 En ligne : https://doi.org/10.1145/3503161.3547746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101906 Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
Titre : Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions Titre original : Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions Type de document : Thèse/HDR Auteurs : Joseph Gesnouin, Auteur ; Fabien Moutarde, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2022 Importance : 171 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, Spécialité
Informatique temps réel, robotique et automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] piéton
[Termes IGN] reconnaissance de gestes
[Termes IGN] réseau neuronal de graphes
[Termes IGN] squelettisation
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The autonomous vehicle (AV) is a major challenge for the mobility of tomorrow. Progress is being made every day to achieve it; however, many problems remain to be solved to achieve a safe outcome for the most vulnerable road users (VRUs). One of the major challenge faced by AVs is the ability to efficiently drive in urban environments. Such a task requires interactions between autonomous vehicles and VRUs to resolve traffic ambiguities. In order to interact with VRUs, AVs must be able to understand their intentions and predict their incoming actions. In this dissertation, our work revolves around machine learning technology as a way to understand and predict human behaviour from visual signals and more specifically pose kinematics. Our goal is to propose an assistance system to the AV that is lightweight, scene-agnostic that could be easily implemented in any embedded devices with real-time constraints. Firstly, in the gesture and action recognition domain, we study and introduce different representations for pose kinematics, based on deep learning models as a way to efficiently leverage their spatial and temporal components while staying in an euclidean grid-space. Secondly, in the autonomous driving domain, we show that it is possible to link the posture, the walking attitude and the future behaviours of the protagonists of a scene without using the contextual information of the scene (zebra crossing, traffic light...). This allowed us to divide by a factor of 20 the inference speed of existing approaches for pedestrian intention prediction while keeping the same prediction robustness. Finally, we assess the generalization capabilities of pedestrian crossing predictors and show that the classical train-test sets evaluation for pedestrian crossing prediction, i.e., models being trained and tested on the same dataset, is not sufficient to efficiently compare nor conclude anything about their applicability in a real-world scenario. To make the research field more sustainable and representative of the real advances to come. We propose new protocols and metrics based on uncertainty estimates under domain-shift in order to reach the end-goal of pedestrian crossing behavior predictors: vehicle implementation. Note de contenu : 1- Introduction
2- Human activity recognition with pose-driven deep learning models
3- From action recognition to pedestrian discrete intention prediction
4- Assessing the generalization of pedestrian crossing predictors
5- ConclusionNuméro de notice : 24066 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique temps réel, robotique et automatique : Paris Sciences et Lettres : 2022 DOI : sans En ligne : https://tel.hal.science/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091 Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])
[article]
Titre : Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image Type de document : Article/Communication Auteurs : Efosa Gbenga Adagbasa, Auteur ; Samuel Adelabu, Auteur ; Tom W. Okello, Auteur Année de publication : 2022 Article en page(s) : pp 142 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] distribution spatiale
[Termes IGN] espèce végétale
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] MNS ASTER
[Termes IGN] montagne
[Termes IGN] PoaceaeRésumé : (auteur) Understanding the spatial distribution of vegetation species is essential to gain knowledge on the recovery process of an ecosystem. Few studies have used deep learning and machine learning models for image processing focusing on forest/crop classification. This study, therefore, makes use of a multi-layer perceptron (MLP) deep neural network to discriminate grass species in a mountainous region using Sentinel-2 images. Vegetation indices, Sentinel-1 and ASTER DEM were combined with Sentinel-2 images to improve classification accuracy. Stratified K-fold was used to ensure balanced training and test data. The results, when compared with other commonly used machine learning models, outperformed them all. It produced a better discriminate of the grass species when ASTER DEM was combined with Sentinel-2 images, with overall F1 score of 92%. The results of the species discrimination show a general increase in increaser II species such as Eragrostis curvula and a decrease in decreaser species like Phragmites australis. Numéro de notice : A2022-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1704070 En ligne : https://doi.org/10.1080/10106049.2019.1704070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100378
in Geocarto international > vol 37 n° 1 [01/01/2022] . - pp 142 - 162[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Applications of multi-image remote sensing Type de document : Thèse/HDR Auteurs : Roger Mari Molas, Auteur ; Gabriele Facciolo, Directeur de thèse ; Enric Meinhardt-Llopis, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2022 Importance : 191 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l’Université Paris-Saclay, spécialité Mathématiques AppliquéesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] compensation par faisceaux
[Termes IGN] image satellite
[Termes IGN] image Worldview
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] modèle stéréoscopique
[Termes IGN] Python (langage de programmation)
[Termes IGN] reconstruction 3DIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis studies the problem of 3D reconstruction from a collection of high-resolution satellite images. Satellite multi-view 3D reconstruction requires a very fine control of the acquisition geometry, in order to guarantee the consistency of altitude estimates obtained from different views. The first part of the thesis is therefore devoted to the optimization of the mathematical representation of the acquisition geometry, which usually takes the form of RPC camera models. We propose a bundle adjustment methodology that maximizes the geometric consistency between a set of satellite views and the associated RPC cameras. This methodology incorporates an RPC estimation algorithm that allows the direct composition of the original unrefined models with corrective transformations, without using approximate intermediate representations. The second part of the thesis presents different practical applications of multi-image remote sensing, most of which benefit from the consistency control of the acquisition geometry. The different methods concern the following topics: the detection of volume changes on the Earth's surface across different dates; the geometrically consistent generation of large-scale mosaics built from smaller satellite images; a neural rendering network (NeRF) capable of learning the geometry of a satellite scene in a self-supervised manner and also of synthesizing new realistic views, with the ability to distinguish shadows and transient objects from permanent structures; and a comparison between classic algorithms and supervised deep learning networks for dense stereo matching. As a result, this thesis describes a variety of cutting-edge ideas on the exploitation of optical satellite images that have the potential to improve activities related to large-scale land surface knowledge, such as surveillance, urban planning or natural resource management. The presented methods are evaluated with high-resolution images from the WorldView-3 and SkySat constellations. The implementation of most methods is also released as open-source Python code. Note de contenu : 1- Introduction
2- Introduction (en français)
Part I. Geometric modeling of multi-view satellite imagery
3- Geolocation correction methods for satellite multi-view stereo
4- Bundle adjustment of RPC camera models
5- Robust RPC camera modeling
Part II. Applications of multi-view satellite imagery
6- Automatic stockpile volume monitoring
7- Perfect sensor localization for push-frame image stitching
8- Satellite NeRF
9- Disparity estimation network
10- ConclusionNuméro de notice : 24100 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Saclay : 2022 Organisme de stage : Centre Borelli (Saclay) DOI : sans En ligne : https://www.theses.fr/2022UPASM045 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102575 Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse / Nesrine Farhani (2022)PermalinkApprentissage profond pour l'imagerie SAR : du débruitage à l'interprétation de scène / Emanuele Dalsasso (2022)PermalinkAutomatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments / Daniela Craciun (2022)PermalinkBuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation / Minh On Vu Ngoc (2022)PermalinkCalibration radiométrique et géométrique d'une caméra fish-eye pour la mesure de l'hémisphère de luminance incidente / Manchun Lei (2022)PermalinkCartographie dynamique de la topographie de l'océan de surface par assimilation de données altimétriques / Florian Le Guillou (2022)PermalinkCharacteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)PermalinkClassification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkContribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)PermalinkPermalinkPermalinkPermalinkPermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPermalinkDetection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks / Stefan Reder in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkDevelopment of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)PermalinkDéveloppement d’outils et de méthodes permettant l’acquisition, le traitement et la diffusion de données issues de levés par drone / Guillaume Feuillatre (2022)PermalinkPermalink