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Titre : DeepSim-Nets: Deep Similarity Networks for stereo image matching Type de document : Article/Communication Auteurs : Mohamed Ali Chebbi, Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Paul Lopes, Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 2096 - 2104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] géométrie de l'image
[Termes IGN] géométrie épipolaire
[Termes IGN] réseau neuronal profondIndex. décimale : 35.20 Traitement d'image Résumé : (auteur) We present three multi-scale similarity learning architectures, or DeepSim networks. These models learn pixel-level matching with a contrastive loss and are agnostic to the geometry of the considered scene. We establish a middle ground between hybrid and end-to-end approaches by learning to densely allocate all corresponding pixels of an epipolar pair at once. Our features are learnt on large image tiles to be expressive and capture the scene's wider context. We also demonstrate that curated sample mining can enhance the overall robustness of the predicted similarities and improve the performance on radiometrically homogeneous areas. We run experiments on aerial and satellite datasets. Our DeepSim-Nets outperform the baseline hybrid approaches and generalize better to unseen scene geometries than end-to-end methods. Our flexible architecture can be readily adopted in standard multi-resolution image matching pipelines. The code is available at https://github.com/DaliCHEBBI/DeepSimNets. Numéro de notice : C2023-007 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Chebbi_DeepSim- [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103281 PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)
Titre : PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 6526 - 6535 Note générale : bibliographie
voir aussi https://openaccess.thecvf.com/content/CVPR2023W/PCV/supplemental/Wu_PSMNet-FusionX3_LiDAR-Guided_Deep_CVPRW_2023_supplemental.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne à axe vertical
[Termes IGN] scène 3D
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Dense image matching (DIM) and LiDAR are two complementary techniques for recovering the 3D geometry of real scenes. While DIM provides dense surfaces, they are often noisy and contaminated with outliers. Conversely, LiDAR is more accurate and robust, but less dense and more expensive compared to DIM. In this work, we investigate learning-based methods to refine surfaces produced by photogrammetry with sparse LiDAR point clouds. Unlike the current state-of-the-art approaches in the computer vision community, our focus is on aerial acquisitions typical in photogrammetry. We propose a densification pipeline that adopts a PSMNet backbone with triangulated irregular network interpolation based expansion, feature enhancement in cost volume, and conditional cost volume normalization, i.e. PSMNet-FusionX3. Our method works better on low density and is less sensitive to distribution, demonstrating its effectiveness across a range of LiDAR point cloud densities and distributions, including analyses of dataset shifts. Furthermore, we have made both our aerial (image and disparity) dataset and code available for public use. Further information can be found at https://github.com/ whuwuteng/PSMNet-FusionX3. Numéro de notice : C2023-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/PCV/papers/Wu_PSMNet-FusionX3_Li [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103277 Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)
Titre : Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments Type de document : Article/Communication Auteurs : Yuhao Jiang, Auteur ; María-Jesús Lobo , Auteur ; Sidonie Christophe , Auteur ; Christophe Jouffrais, Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2023 Collection : AGILE GIScience Series num. 4 Conférence : AGILE 2023, 26th international AGILE Conference on Geographic Information Science, Spatial data for design 13/06/2023 16/06/2023 Delft Pays-Bas OA Proceedings Importance : n° 29 ; 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] carrefour
[Termes IGN] carte sur mesure
[Termes IGN] carte tactile
[Termes IGN] chaîne de traitement
[Termes IGN] OpenStreetMap
[Termes IGN] personne malvoyanteIndex. décimale : 39.00 Cartographie - généralités - Cartologie Résumé : (auteur) Street intersections are very challenging for people with visual impairments. Manually produced tactile maps are an important support in teaching and assisting independent journeys as they can be customized to serve the visually impaired audience with diverse tactile reading and mobility skills in different use scenarios. But the manual map production involves a huge workload that makes the maps less accessible. This paper explores the possibility of semi-automatically producing customizable tactile maps for street intersections. It presents a parameterized semi-automated pipeline based on OSM data that allows the maps to be customized in size, map features, geometry processing choices, and symbolizations. It produces street intersection maps in two scales of three sizes, with different levels of details and styles. Numéro de notice : C2023-013 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-4-29-2023 Date de publication en ligne : 06/06/2023 En ligne : https://doi.org/10.5194/agile-giss-4-29-2023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103307 A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery / Sajid Ghuffar in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
Titre : A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery Type de document : Article/Communication Auteurs : Sajid Ghuffar, Auteur ; Tobias Bolch, Auteur ; Ewelina Rupnik , Auteur ; Atanu Bhattacharya, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie
voir aussi https://research-repository.st-andrews.ac.uk/bitstream/10023/26124/1/Ghuffar_2022_IEEE_TGRS_Pipeline_automated_processing_AAM.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compensation par faisceaux
[Termes IGN] géométrie de l'image
[Termes IGN] géométrie épipolaire
[Termes IGN] glacier
[Termes IGN] Himalaya
[Termes IGN] image Corona
[Termes IGN] image panoramique
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'appuiRésumé : (auteur) The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time dependent exterior orientation parameters is employed. The results show that using the entire frame of the Corona image, bundle adjustment using well-distributed GCPs results in an average standard deviation (SD) of less than 2 pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the accuracy. We further assess a polynomial epipolar resampling method for rectification of Corona stereo images. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long term storage as likely cause of systematic deviations. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation (NMAD) of elevation differences of ? 4m over an area of approx. 4000km2. We show that the proposed pipeline can be applied to sequence of complex scenes involving high relief and glacierized terrain and that the resulting DEMs can be used to compute long term glacier elevation changes over large areas. Numéro de notice : A2022-952 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3200151 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3200151 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103286
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - pp[article]3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)
[article]
Titre : 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation Type de document : Article/Communication Auteurs : Heyang Thomas Li, Auteur ; Zachary Todd, Auteur ; Nikolas Bielski, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1759 - 1774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espace image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] route
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] signalisation routièreRésumé : (auteur) The classification and extraction of road markings and lanes are of critical importance to infrastructure assessment, planning and road safety. We present a pipeline for the accurate segmentation and extraction of rural road surface objects in 3D lidar point-cloud, as well as a method to extract geometric parameters belonging to tar seal. To decrease the computational resources needed, the point-clouds were aggregated into a 2D image space before being transformed using affine transformations. The Mask R-CNN algorithm is then applied to the transformed image space to localize, segment and classify the road objects. The segmentation results for road surfaces and markings can then be used for geometric parameter estimation such as road widths estimation, while the segmentation results show that the efficacy of the existing Mask R-CNN to segment needle-type objects is improved by our proposed transformations. Numéro de notice : A2022-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02103-8 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02103-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100627
in The Visual Computer > vol 38 n° 5 (May 2022) . - pp 1759 - 1774[article]Evaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)PermalinkLandslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)PermalinkLearning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)PermalinkPermalinkFeature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac / Lulin Zhang in Blog de la RFPT, sans n° ([17/11/2021])PermalinkFeature matching for multi-epoch historical aerial images / Lulin Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkFully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods / Ferdinand Maiwald in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkMobile mapping et PCRS / Clément Benoît in Géomatique expert, n° 136 (novembre - décembre 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkMarrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkScalable deep learning to identify brick kilns and aid regulatory capacity / Jihyeon Lee in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 118 n° 17 (27 April 2021)PermalinkA user-driven process for INSPIRE-compliant land use database: example from Wallonia, Belgium / Benjamin Beaumont in Annals of GIS, vol 27 n° 2 (April 2021)PermalinkPassive radar imaging of ship targets with GNSS signals of opportunity / Debora Pastina in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkPermalinkAutomatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)PermalinkCombining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)PermalinkPermalinkPermalinkPermalinkFrom point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)PermalinkPermalinkGeometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)PermalinkIntégration et analyse de données massives et hétérogènes pour une observation intelligente du territoire / Rodrigue Kafando (2021)PermalinkProgrammation d’un système de scannage multiple pilotable et mise en place de tests de qualité pour l’optimisation d’une chaîne de traitement photogrammétrique / Augustin Cosson (2021)PermalinkPermalinkVectorization of historical maps using deep edge filtering and closed shape extraction / Yizi Chen (2021)PermalinkVers un protocole de calibration de caméras statiques à l'aide d'un drone / Jean-François Villeforceix (2021)PermalinkDu drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)PermalinkQuality assessment of photogrammetric methods - A workflow for reproducible UAS orthomosaics / Marvin Ludwig in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkBayesian transfer learning for object detection in optical remote sensing images / Changsheng Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkA generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkTopographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkPrivacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation / Alexander Dunkel in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkAutomated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)PermalinkPermalinkPermalinkDiagnostic qualité et apurement des données de mobilité quotidienne issues de l’enquête mixte et longitudinale Mobi’Kids / Sylvestre Duroudier in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)PermalinkLearning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)PermalinkLightweight temporal self-attention for classifying satellite images time series / Vivien Sainte Fare Garnot (2020)PermalinkPermalinkOptimiser la gestion conjointe de la voirie et des réseaux enterrés à l'aide de la géomatique : conception d'un référentiel spatial de voirie / Antonin Pavard (2020)PermalinkPermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)PermalinkContext pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkChange detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkLearning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkThe Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment / Michele Manunta in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkTime-lapse photogrammetry of distributed snow depth during snowmelt / Simon Filhol in Water resources research, vol 55 n° 9 (September 2019)PermalinkValidating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 46 n° 5 (September 2019)PermalinkUne nouvelle méthode de vectorisation du cadastre ancien / Antony Chalais in Géomatique expert, n° 129 (août - septembre 2019)PermalinkAutomatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)PermalinkDéveloppement d’un « ModelBuilder » pour l’évaluation de la recharge nette : cas de la nappe phréatique de Zéramdine Beni Hassène (Tunisie) / Imen Hentati in Géomatique expert, n° 128 (juin - juillet 2019)PermalinkEstimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)PermalinkOn the positional accuracy and maximum allowable scale of UAV-derived photogrammetric products for archaeological site documentation / Juan Antonio Pérez in Geocarto international, vol 34 n° 6 ([15/05/2019])PermalinkDigital surface model generation from high resolution multi-view stereo satellite imagery / Ke Gong in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 5 (May 2019)PermalinkAn image-pyramid-based raster-to-vector conversion (IPBRTVC) framework for consecutive-scale cartography and synchronized generalization of classic objects / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkMise en place de procédures automatisées pour les reports topographiques en milieu ferroviaire à partir de données photogrammétriques et LiDAR acquises par drones / Marion Hinaux in XYZ, n° 158 (mars 2019)PermalinkNumérisation et modélisation 3D du Jardin d’Hiver du Musée de la Faïence de Sarreguemines / Valentin Girardet in XYZ, n° 158 (mars 2019)PermalinkDiffusion and inpainting of reflectance and height LiDAR orthoimages / Pierre Biasutti in Computer Vision and image understanding, vol 179 (February 2019)PermalinkAnalysis of the usability of mobile laser scanning data in snowy conditions / Mathilde Letard (2019)PermalinkArchival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)PermalinkDétection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)PermalinkGround displacement measurements / Louis-Marie Gauer (2019)PermalinkMeasuring stem diameters with TLS in boreal forests by complementary fitting procedure / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPermalinkVectorisation du cadastre ancien : restructuration de la chaîne de traitement, implémentation d’une nouvelle méthode de détection et utilisation de la théorie des graphes / Antony Chalais (2019)Permalink3D urban geovisualization: in situ augmented and mixed reality experiments / Alexandre Devaux in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-4 (October 2018)PermalinkDéveloppement d'une procédure d'amélioration du calcul de trajectographie d'un système de cartographie dynamique / Katia Mirande in XYZ, n° 156 (septembre - novembre 2018)PermalinkPedestrian network information extraction based on VGI / Xuejing Xie in Geomatica, vol 72 n° 3 (September 2018)PermalinkA context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkVers une remise en géométrie automatique des prises de vue aériennes historiques photogrammétriques / Arnaud Le Bris in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkLarge scale textured mesh reconstruction from mobile mapping images and LIDAR scans / Mohamed Boussaha in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)PermalinkContextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkAdéquation algorithme architecture pour la localisation basée image sur système embarqué / David Vandergucht (2018)PermalinkAn (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018)PermalinkChaîne de traitement de photogrammétrie en vue de réaliser un MNS à partir de photographies aériennes / Alice Gonnaud (2018)PermalinkEtude préalable à l'installation d'un coin radar sur le site de co-localisation de Calern / Guillaume Schmidt (2018)PermalinkEvaluation of photogrammetric block orientation using quality descriptors from statistically filtered tie points / Alessio Calantropio (2018)PermalinkEvaluation des performances des modèles numérique d’élévation issus de l’imagerie tri-stéréo Pléiades pour le suivi de l’évolution morphologique des dunes littorales / Mannaïg L'haridon (2018)PermalinkGenerating terrestrial glacier views from historic airphotos for comparison with contemporary ground photographs / Marion Holst (2018)PermalinkRaffinement de la localisation d’images provenant de sites participatifs pour la mise à jour de SIG urbain / Bernard Semaan (2018)PermalinkTesting, analysis and improvement of FGI-NLS Sentinel-2 data processing chain for land use applications / Emile Blettery (2018)PermalinkProcess BIM : Une chaîne de traitements pour le tel que construit / Tania Landes in Géomètre, n° 2146 (avril 2017)PermalinkAutomatic production of large-scale cloud-free orthomosaics from multitemporal satellite images / Nicolas Champion (2017)PermalinkCaractérisation de la végétation de Rennes Métropole par relevé LiDAR en vue de sa modélisation / Clément Doceul (2017)PermalinkFully automatic analysis of archival aerial images : Current status and challenges / Sébastien Giordano (2017)PermalinkGéomatique et géo-décisionnel appliqués au Référentiel des territoires du département de l’Hérault / Stanislas Cheptou (2017)PermalinkDu nuage de points à la maquette numérique de bâtiment : reconstruction 3D semi-automatique de bâtiments existants / Hélène Macher (2017)PermalinkSecond iteration of photogrammetric pipeline to enhance the accuracy of image pose estimation / Truong Giang Nguyen (2017)PermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (2017)PermalinkUtilisation d’un modèle numérique de hauteur en stratification des données de l’Inventaire Forestier National / Sophie Georges (2017)Permalink