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Termes IGN > mathématiques > statistique mathématique
statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers / Seyyed Mohammadreza Rahimi in Geoinformatica, vol 26 n° 1 (January 2022)
[article]
Titre : Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers Type de document : Article/Communication Auteurs : Seyyed Mohammadreza Rahimi, Auteur ; Behrouz Far, Auteur ; Xin Wang, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse spatiale
[Termes IGN] comportement
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] covariance
[Termes IGN] données spatiotemporelles
[Termes IGN] historique des données
[Termes IGN] interface web
[Termes IGN] mobilité territoriale
[Termes IGN] prise en compte du contexte
[Termes IGN] réseau social géodépendant
[Termes IGN] service fondé sur la position
[Termes IGN] système de recommandationRésumé : (auteur) Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the users to contextual variables based on their own history and the history of similar behaving users. In this paper, we propose a contextual location recommendation method named Contextual Location Recommendation (CLR) that learns the intention and spatial responses of users to various contextual triggers using the historical check-in and contextual information. CLR starts with a co-variance analysis to reduce dimensionality of the check-in data and then uses an optimized version of the random walk with restart to extract hidden user responses to contextual triggers. A tensor factorization is used to build a latent-factor model to predict the user’s intention response with the given set of contextual triggers. Based on the intention response of the user, a contextual spatial component identifies a set of matching locations accessible to the user by estimating the probability distribution of the location of the user and the popularity probability of locations under the contextual settings. Experimental results on three real-world datasets show that CLR improves the recommendation precision by 35% compared to the best-performing baseline recommendation method. Numéro de notice : A2022-203 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00437-y Date de publication en ligne : 02/06/2021 En ligne : https://doi.org/10.1007/s10707-021-00437-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100008
in Geoinformatica > vol 26 n° 1 (January 2022) . - pp 1 - 28[article]Contraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)
Titre : Contraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer Type de document : Thèse/HDR Auteurs : Jonathan Chenal , Auteur ; Benoit Meyssignac, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] chaleur
[Termes IGN] changement climatique
[Termes IGN] dioxyde de carbone
[Termes IGN] erreur systématique
[Termes IGN] montée du niveau de la mer
[Termes IGN] niveau de la mer
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] température de surface de la merIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Ma thèse explore l'estimation observationnelle de la sensibilité climatique d'équilibre (ECS) à partir des données instrumentales historiques et récentes, en particulier spatiales. L'ECS est la température globale moyenne de surface de la Terre atteinte après un doublement instantané de la concentration de dioxyde de carbone atmosphérique par rapport à la concentration préindustrielle. Cette quantité est la métrique fondamentale de l'amplitude du changement climatique contemporain, car l'évolution actuelle et future de nombreuses variables du système climatique, comme la température ou l'expansion thermique de l'océan, lui est fortement corrélée. L'ECS demeure cependant mal connue, puisque depuis le rapport Charney de 1979 jusqu'au Cinquième rapport d'évaluation du Groupe intergouvernemental d'experts sur le climat (GIEC), sa plage de valeurs probables se situait entre 1,5 et 4,5 degrés, avec un biais marqué entre estimations instrumentales, qui se situent dans la partie basse de l'intervalle, et estimations issues des modèles de climat, qui se situent dans la partie haute de l'intervalle. La cause principale de cette dispersion réside dans le fait que la sensibilité climatique varie avec le temps, en lien notamment avec l'effet radiatif des structures spatiales du réchauffement de surface, qui peuvent changer au cours du temps du fait de la variabilité interne du climat ou des variations historiques dans le type de forçage. Ma thèse utilise des séries temporelles observationnelles récentes de contenu de chaleur de l'océan et de température de surface, et une reconstruction récente de forçage radiatif, pour estimer le paramètre de rétroaction climatique, puis l'ECS, par régression linéaire de l'équation du bilan d'énergie planétaire. Dans ce processus, je prends en compte toutes les sources d'erreur et je les propage dans la régression afin d'obtenir une description exhaustive de l'incertitude observationnelle associée à l'ECS. Par ailleurs, j'utilise les variations temporelles du paramètre de rétroaction climatique simulées par les modèles de climat pour évaluer les biais et incertitudes associés à ce paramètre et dus à la structure spatiale du réchauffement historique. Sur la base du bilan d'énergie ainsi résolu sur 1971-2017, je démontre qu'il est très peu probable (p Numéro de notice : 17769 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Organisme de stage : Laboratoire d'Etudes en Géophysique et Océanographie Spatiales LEGOS nature-HAL : Thèse DOI : sans En ligne : http://thesesups.ups-tlse.fr/5598/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103338 Contribution 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)
Titre : Contribution 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 Type de document : Thèse/HDR Auteurs : Calimanut-Ionut Cira, Auteur Editeur : Madrid [Espagne] : Universidad politécnica de Madrid Année de publication : 2022 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat en Topographie, Géodésie et cartographie, Universidad politécnica de MadridLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] route
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. Remote sensing experts have been actively using deep neural networks to solve object extraction tasks in high-resolution aerial imagery by means of supervised operations. However, the extraction operation is imperfect, due to the nature of remotely sensed images (noise, obstructions, etc.), the limitations of sensing resolution, or the occlusions often present in the scenes. The road network plays an important part in transportation and, nowadays, one of the main related challenges is keeping the existent cartographic support up to date. This task can be considered very challenging due to the complex nature of the geospatial object (continuous, with irregular geometry, and significant differences in width). We also need to take into account that secondary roads represent the largest part of the road transport network, but due to the absence of clearly defined edges, and the different spectral signatures of the materials used for pavement, monitoring, and mapping them represents a great effort for public administration, and their extraction is often omitted altogether. We believe that recent advancements in machine vision can enable a successful extraction of the road structures from high-resolution, remotely sensed imagery and a greater automation of the road mapping operation. In this PhD thesis, we leverage recent computer vision advances and propose a deep learning-based end-to-end solution, capable of efficiently extracting the surface area of roads at a large scale. The novel approach is based on a disjoint execution of three different image processing operations (recognition, semantic segmentation, and post-processing with conditional generative learning) within a common framework. We focused on improving the state-of-the-art results for each of the mentioned components, before incorporating the resulting models into the proposed solution architecture. For the recognition operation, we proposed two framework candidates based on convolutional neural networks to classify roads in openly available aerial orthoimages divided in tiles of 256×256 pixels, with a spatial resolution of 0.5 m. The frameworks are based on ensemble learning and transfer learning and combine weak classifiers to leverage the strengths of different state-of-the-art models that we heavily modified for computational efficiency. We evaluated their performance on unseen test data and compared the results with those obtained by the state-of-the-art convolutional neural networks trained for the same task, observing improvements in performance metrics of 2-3%. Secondly, we implemented hybrid semantic segmentation models (where the default backbones are replaced by neural network specialised in image segmentation) and trained them with high-resolution remote sensing imagery and their correspondent ground-truth masks. Our models achieved mean increases in performance metrics of 2.7-3.5%, when compared to the original state-of-the-art semantic segmentation architectures trained from scratch for the same task. The best-performing model was integrated on a web platform that handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format, and the generation of GeoTIFF results (compatible with geospatial databases). Thirdly, the road surface area extraction task is generally carried out via semantic segmentation over remotely sensed imagery—however, this supervised learning task can be considered very costly because it requires remote sensing images labelled at pixel level and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). We consider that unsupervised learning (not requiring labelled data) can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. For this reason, we also approached the post-processing of the road surface areas obtained with the best performing segmentation model to improve the initial segmentation predictions. In this line, we proposed two post-processing operations based on conditional generative learning for deep inpainting and image-to-image translation operations and trained the networks to learn the distribution of the road network present in official cartography, using a novel dataset covering representative areas of Spain. The first proposed conditional Generative Adversarial Network (cGAN) model was trained for deep inpainting operation and obtained improvements in performance metrics of maximum 1.3%. The second cGAN model was trained for image-to-image translation, is based on a popular model heavily modified for computational efficiency (a 92.4% decrease in the number of parameters in the generator network and a 61.3% decrease in the discriminator network), and achieved a maximum increase of 11.6% in performance metrics. We also conducted a qualitative comparison to visually assess the effectiveness of the generative operations and observed great improvements with respect to the initial semantic segmentation predictions. Lastly, we proposed an end-to-end processing strategy that combines image classification, semantic segmentation, and post-processing operations to extract containing road surface area extraction from high-resolution aerial orthophotography. The training of the model components was carried out on a large-scale dataset containing more than 537,500 tiles, covering approximately 20,800 km2 of the Spanish territory, manually tagged at pixel level. The consecutive execution of the resulting deep learning models delivered higher quality results when compared to state-of-the-art implementations trained for the same task. The versatility and flexibility of the solution given by the disjointed execution of the three separate sub-operations proved its effectiveness and economic efficiency and enables the integration of a web application that alleviates the manipulation of geospatial data, while allowing for an easy integration of future models and algorithms. Resuming, applying the proposed models resulted from this PhD thesis translates to operations aimed to check if the latest existing aerial orthoimages contains the studied continuous geospatial element, to obtain an approximation of its surface area using supervised learning and to improve the initial segmentation results with post-processing methods based on conditional generative learning. The results obtained with the proposed end-to-end-solution presented in this PhD thesis improve the state-of-the-art in the field of road extraction with deep learning techniques and prove the appropriateness of applying the proposed extraction workflow for a more robust and more efficient extraction operation of the road transport network. We strongly believe that the processing strategy can be applied to enhance other similar extraction tasks of continuous geospatial elements (such as the mapping of riverbeds, or railroads), or serve as a base for developing additional extraction workflows of geospatial objects from remote sensing images. Note de contenu : 1- Introduction
2- Methodology
3- Theoretical framework
4- Litterature review
5- Road recognition: A framework based on nestion of convolutional neuronal networks and transfer learning to regognise road elements
6- Road segmentation: An approach based on hybrid semantic segmentation models to extract the surface area of rod elements from aerial orthoimagery
7- Post-processing of semantic segmentation predictions I: A conditional generative adversial network to improve the extraction of road surface areas via deep inpainting operations
8- Post-processing of semantic segmentation predictions II: A lightweight conditional generative adversial network to improve the extraction of road surface areas via image-to-image translation
9- An end-to-end road extraction solution based on regonition, segmentation, and post-processing operations for a large-scale mapping of the road transport network from aerial orthophotography
10- ConclusionsNuméro de notice : 24069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Topographie, Géodésie et cartographie : Universidad politécnica de Madrid : 2022 DOI : 10.20868/UPM.thesis.70152 En ligne : https://doi.org/10.20868/UPM.thesis.70152 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102113 Conventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : Conventional and neural network-based water vapor density model for GNSS troposphere tomography Type de document : Article/Communication Auteurs : Chen Liu, Auteur ; Yibin Yao, Auteur ; Chaoqian Xu, Auteur Année de publication : 2022 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur absolue
[Termes IGN] étalonnage de modèle
[Termes IGN] modèle météorologique
[Termes IGN] propagation troposphérique
[Termes IGN] tomographie par GPS
[Termes IGN] vapeur d'eau
[Termes IGN] voxelRésumé : (auteur) Global navigation satellite system (GNSS) water vapor (WV) tomography is a promising technique to reconstruct the three-dimensional (3D) WV field. However, this technique usually suffers from the ill-posed problem caused by the poor geometry of GNSS rays, resulting in underdetermined tomographic equations. Such equations often rely on iterative methods for solving, but conventional iterative approaches require accurate initial WV density. To address this demand, we proposed two models for WV density estimation. One is the conventional model (CO model) that consists of an exponential model and a linear least-squares model, which are used to describe the spatial and temporal variability of the WV density, respectively. The other is a neural network model (NN model) that uses a backpropagation neural network (BPNN) to fit the nonlinear variation of WV density in both spatial and temporal domains. WV density derived from a Hong Kong (HK) radiosonde station (RS) during 2020 was used to validate the proposed models. Validation results show that both models well describe the spatial and temporal distribution of the WV density. The NN model exhibits better prediction performance than the CO model in terms of root mean square error (RMSE) and bias. We also applied the proposed models to GNSS WV tomography to test their performance in extreme weather conditions. Test results show that the proposed model-based GNSS tomography can correct the content of WV density but cannot accurately sense its irregular distribution. Numéro de notice : A2022-005 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01188-x Date de publication en ligne : 23/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01188-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98920
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 4[article]
Titre : Cross-dataset learning for generalizable land use scene classification Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : EarthVision 2022, Large Scale Computer Vision for Remote Sensing Imagery, workshop joint to CVPR 2022 19/06/2022 24/06/2022 New Orleans Louisiane - Etats-Unis OA Proceedings Importance : pp 1382 - 1391 Note générale : bibliographie
in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1382-1391Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cadre conceptuel
[Termes IGN] descripteur
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] intelligence artificielle
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du solRésumé : (auteur) Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or uneen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training. Numéro de notice : C2022-031 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers IEEE Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/CVPRW56347.2022.00144 En ligne : https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Gominski_Cros [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101087 Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkDART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)PermalinkDeep image translation with an affinity-based change prior for unsupervised multimodal change detection / Luigi Tommaso Luppino in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkDetecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)PermalinkPermalinkPermalinkDetection 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)PermalinkPermalinkEffective triplet mining improves training of multi-scale pooled CNN for image retrieval / Federico Vaccaro in Machine Vision and Applications, vol 33 n° 1 (January 2022)PermalinkÉléments pour l'analyse et le traitement d'images : application à l'estimation de la qualité du bois / Rémy Decelle (2022)PermalinkEmpirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed / Ropesh Goyal in Survey review, vol 54 n° 382 (January 2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)PermalinkEstimation of Lesser Antilles vertical velocity fields using a GNSS-PPP software comparison / Pierre Sakic-Kieffer (2022)PermalinkPermalinkEvaluation de méthodes automatisées de cartographie des zones inondables adaptées à la prévision des crues soudaines / Nabil Hocini (2022)PermalinkÉvolution rétrospective et prospective d’un massif dunaire par imagerie multispectrale et LiDAR / Iris Jeuffrard (2022)PermalinkExploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)PermalinkFLAIR: French Land cover from Aerial ImageRy - Challenge FLAIR #1: semantic segmentation and domain adaptation / Anatol Garioud (2022)PermalinkForest fire susceptibility assessment using Google Earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkPermalinkFusion de données hyperspectrales et panchromatiques dans le domaine réflectif / Yohann Constans (2022)PermalinkUne généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2022)PermalinkGenerating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)PermalinkGénération d’un jeu de données d’entraînement et mise en oeuvre d’une architecture de détection par deep learning des numéros de parcelles sur les plans du cadastre Napoléonien / Tiecoumba Ibrahim Tamela (2022)PermalinkPermalinkA GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)PermalinkHistograms of oriented mosaic gradients for snapshot spectral image description / Lulu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkHistorical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)PermalinkHourly rainfall forecast model using supervised learning algorithm / Qingzhi Zhao in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkIdentifying map users with eye movement data from map-based spatial tasks: user privacy concerns / Hua Liao in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkImproving local adaptive filtering method employed in radiometric correction of analogue airborne campaigns / Lâmân Lelégard (2022)PermalinkImproving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkIncorporation of spatial anisotropy in urban expansion modelling with cellular automata / Jinqu Zhang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkPermalinkInteractive semantic segmentation of aerial images with deep neural networks / Gaston Lenczner (2022)PermalinkIntroduction à la géomatique pour le statisticien : quelques concepts et outils innovants de gestion, traitement et diffusion de l’information spatiale / François Sémécurbe (2022)PermalinkLearning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)PermalinkLearning spatio-temporal representations of satellite time series for large-scale crop mapping / Vivien Sainte Fare Garnot (2022)Permalink