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Titre : AI4GEO: LOD0 Generation for 3D building models Type de document : Article/Communication Auteurs : Pierre Lassalle, Auteur ; Bruno Vallet , Auteur ; Etienne Le Bihan, Auteur ; Pierre-Marie Brunet, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2023 Projets : AI4GEO / Conférence : JURSE 2023, Joint Urban Remote Sensing Event 17/05/2023 19/05/2023 Heraklion Grèce Proceedings IEEE Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] image satellite
[Termes IGN] niveau de détail
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] zone urbaineRésumé : (Auteur) Recent studies on Earth observation are improved by the proliferation of imaging sensors able to capture large datasets with a high spatial resolution. As a result, many approaches have been proposed for 3D modeling, remote sensing (RS), image processing and mapping. In this scope, three-dimensional (3D) mapping of urban areas has a great potential to provide the user with a precise scene understanding. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information with new added-value services. This paper will first introduce the AI4GEO initiative, context and overall objectives. It will then present the current status regarding 3D reconstruction of urban areas, in particular LOD0 building shape generation using satellite data. Numéro de notice : C2023-010 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE57346.2023.10144155 Date de publication en ligne : 08/06/2023 En ligne : https://doi.org/10.1109/JURSE57346.2023.10144155 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103311
Titre : AI4GEO: A path from 3D model to digital twin Type de document : Article/Communication Auteurs : Pierre-Marie Brunet, Auteur ; Simon Baillarin, Auteur ; Pierre Lassalle, Auteur ; Flora Weissgerber, Auteur ; Bruno Vallet , Auteur ; Christophe Triquet, Auteur ; Gilles Foulon, Auteur ; Gaëlle Romeyer , Auteur ; Gwénaël Souillé, Auteur ; Laurent Gabet, Auteur ; Cedrik Ferrero, Auteur ; Thanh-Long Huynh, Auteur ; Emeric Lavergne, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : AI4GEO / Conférence : IGARSS 2022, IEEE International Geoscience And Remote Sensing Symposium 17/07/2022 22/07/2022 Kuala Lumpur Malaysie Proceedings IEEE Importance : pp 4728 - 4731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] CityGML
[Termes IGN] données localisées 3D
[Termes IGN] jumeau numérique
[Termes IGN] segmentation sémantique
[Termes IGN] ville intelligenteRésumé : (auteur) 3D Geospatial information plays a key role in many soaring sectors such as sustainable and smart cities, climate monitoring, ecological mobility, and economic intelligence. The availability of huge volumes of satellite, airborne and insitu data now makes this production feasible at large scale. It needs nonetheless a certain level of manual intervention to secure the level of quality, which prevents mass production. This paper presents the AI4GEO program that aims at developing an end to end solution to produce automatically qualified 3D Digital model at scale together with multiple layers of information. Numéro de notice : C2022-040 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS46834.2022.9883433 Date de publication en ligne : 28/09/2022 En ligne : https://doi.org/10.1109/IGARSS46834.2022.9883433 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101768
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
Titre : Deep-learning based multiple land-cover map translation Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 1-Pas de projet / Gouet-Brunet, Valérie Conférence : IGARSS 2022, IEEE International Geoscience And Remote Sensing Symposium 17/07/2022 22/07/2022 Kuala Lumpur Malaysie Proceedings IEEE Importance : pp 1260 - 1263 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'occupation du sol
[Termes IGN] cadre conceptuel
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (auteur) This paper presents a framework for simultaneously translating multiple land-cover maps into a given one in a supervised way. Conversely to existing approaches working on 1–1 translation, we propose a multi-translation setup that increases the generalizability and translation performance, especially on land-cover maps covering restricted spatial extents. The proposed method mainly assumes that the map of interest spatially overlaps at least with one of the other maps. High performance translation is achieved with a Convolutional Neural Network (CNN) based encoder-decoder frame-work trained with three goals: (i) high-quality translation; (ii) self-reconstruction ability; (iii) mapping of all datasets into a common representation space. Country-scale experimental results show the method effectiveness in translating six highly heterogeneous land-cover maps, achieving significantly better results than the traditional semantic-based method and better results than CNN trained for a 1–1 translation task (+ 9.7% in Overall Accuracy (OA) and +12% in macro F1-score (mF1)). Numéro de notice : C2022-039 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : https://hal.science/hal-03983066v1/document Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS46834.2022.9883056 En ligne : https://doi.org/10.1109/IGARSS46834.2022.9883056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101765 In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)
Titre : In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region Type de document : Article/Communication Auteurs : Nadia Ouaadi, Auteur ; Ludovic Villard, Auteur ; Saïd Khabba, Auteur ; Pierre-Louis Frison , Auteur ; Jamal Ezzahar, Auteur ; Mohamed Kasbani, Auteur ; Adnane Chakir , Auteur ; Pascal Fanise, Auteur ; Valérie Le Dantec, Auteur ; Mehrez Zribi, Auteur ; Salah Er-Raki, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Gouet-Brunet, Valérie Conférence : IGARSS 2022, IEEE International Geoscience And Remote Sensing Symposium 17/07/2022 22/07/2022 Kuala Lumpur Malaysie Proceedings IEEE Importance : pp 4951 - 4954 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cohérence photométrique
[Termes IGN] variation diurneRésumé : (auteur) Irrigated agriculture is the largest consumer of freshwater in the world, particularly in the South Mediterranean region that is already suffering from water shortages. Monitoring the water stress status of plants can contribute to an optimal use of irrigation. C-band radar data have shown great potential for monitoring soil and vegetation hydric conditions. While a diurnal cycle up to 1 dB has been observed over tropical forests, the behavior of annual crops is yet to be investigated. In this context, an experiment composed of a radar setup with 6 C-band antennas was installed in Morocco over a wheat field. 15 minutes full polarization acquisitions of the backscattering coefficient and the interferometric coherence are analyzed in relation with the physiological functioning of wheat. In this paper, the first results from the analysis of data collected during the 2020 growing season are presented. The results reveal the existence of a diurnal cycle of the interferometric coherence and the backscattering coefficient (up to 0.45 and 1.5 dB, respectively) with amplitudes increase in relation with vegetation development. Numéro de notice : C2022-041 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS46834.2022.9884289 Date de publication en ligne : 28/09/2022 En ligne : https://doi.org/10.1109/IGARSS46834.2022.9884289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101769 Predicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (2022)PermalinkPermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkAssessment of sky diffuse irradiance and building reflected irradiance in cast shadows / Manchun Lei (2021)PermalinkPermalinkPermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)PermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over a wheat field in a semi-arid area / Nadia Ouaadi (2021)PermalinkPermalinkPermalinkSupplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)PermalinkC band radar crops monitoring at high temporal frequency: first results of the MOCTAR campaign / Pierre-Louis Frison (2020)PermalinkPermalinkPermalinkSurface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)PermalinkTemporal decorrelation at C- and L-band over olive tree plantations: first insights from the Marocscat campaigns / Ludovic Villard (2020)PermalinkTorch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds / Thomas Chaton (2020)PermalinkUnderwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns / Loïca Avanthey (2020)PermalinkUnderwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)PermalinkWater stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter / Nadia Ouaadi (2020)PermalinkAnalysis and modelling of remote sensing reflectance during anoxic crisis in the Thau lagoon using satellite images / Manchun Lei (2019)PermalinkPermalinkPermalinkLU-Net, An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net / Pierre Biasutti (2019)PermalinkPermalinkSensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)PermalinkThe necessary yet complex evaluation of 3D city models: a semantic approach / Oussama Ennafii (2019)PermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkUrban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)PermalinkComparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)PermalinkCrop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)PermalinkDecision fusion of SPOT6 and multitemporal Sentinel2 images for urban area detection / Cyril Wendl (2018)PermalinkDetection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method / Moussa Sofiane Karoui (2018)PermalinkDomain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks / Tristan Postadjian (2018)PermalinkPermalinkPermalinkPotential and limits of Sentinel-1 data for small alpine glaciers monitoring / Matthias Jauvin (2018)PermalinkPermalinkSentinel-2 level-1 calibration and validation status from the mission performance centre / Catherine Bouzinac (2018)PermalinkA stixel approach for enhancing semantic image segmentation using prior map information / Sylvain Jonchery (2018)PermalinkSuperpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)PermalinkAutomatic production of large-scale cloud-free orthomosaics from multitemporal satellite images / Nicolas Champion (2017)PermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkPermalinkPermalinkFully automatic analysis of archival aerial images : Current status and challenges / Sébastien Giordano (2017)PermalinkHierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)PermalinkHow to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)PermalinkPermalinkNew iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)PermalinkUrban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)PermalinkAn assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)PermalinkPermalinkFirst results from the GLORIE polarimetric GNSS-R airborne campaign dedicated to land parameters estimation / Erwan Motte (2016)PermalinkLandmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks / Bahman Soheilian (2016)PermalinkPermalinkPermalinkAssessment of the relevance of information derived from the unmixing of polarimetric radar images / Sébastien Giordano (2015)PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)PermalinkPermalinkFusion of Lidar and SAR data for land-cover mapping in natural environments / Clara Barbanson (2015)PermalinkKite-borne photogrammetry for decimetric 3D mapping of several square kilometres areas / Denis Feurer (2015)PermalinkPermalinkA Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)PermalinkRetrieving the stand age from a retrospective detection of multinannual forest changes using Landsat data. Application on the heavily managed maritime pine forest in Southwestern France from a 30-year Landsat time-series (1984–2014) / Dominique Guyon (2015)PermalinkPermalinkPermalinkAgricultural field delimitation using active learning and random forests margin / Karim Ghariani (2014)PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)PermalinkPermalinkPermalinkIdentify important spectrum bands for classification using importances of wrapper selection applied to hyperspectral data / Arnaud Le Bris (2014)PermalinkIndividual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery / Yuchu Qin (2014)PermalinkLarge scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)PermalinkA unified framework for land-cover database update and enrichment using satellite imagery / Adrien Gressin (2014)PermalinkUnmixing polarimetric radar images based on land cover type before target decomposition / Sébastien Giordano (2014)PermalinkPermalinkUse intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications / Arnaud Le Bris (2014)PermalinkComparison of VHR panchromatic texture features for tillage mapping / Nesrine Chehata (juillet 2013)PermalinkContribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)PermalinkGeneration of an integrated 3D city model with visual landmarks for autonomous navigation in dense urban areas / Bahman Soheilian (June 2013)PermalinkLarge-scale water classification of coastal areas using airborne topographic lidar data / Julien Smeeckaert (juillet 2013)PermalinkMaterial reflectance retrieval in urban tree shadows with physics-based empirical atmospheric correction / Karine R.M. Adeline (2013)PermalinkObject detection and localization using a knowledge graph on spatial relationships / Nguyen-Vu Hoang (July 2013)PermalinkPermalinkPermalinkSingle strata canopy cover estimation using airborne laser scanning data / António Ferraz (juillet 2013)PermalinkPermalinkWhen script engravings reveal a semantic link between the conceptual and the spatial dimensions of a monument: The case of the tomb of Emperor Qianlong / Livio de Luca (2013)PermalinkL-band InSAR decorrelation analysis in volcanic terrains using airborne LiDAR data and in situ measurements: The case of the Piton de la Fournaise volcano, France / Melanie Sedze (2012)PermalinkComparing small-footprint lidar and forest inventory data for single strata biomass estimation : A case study over a multi-layered mediterranean forest / António Ferraz (2012)PermalinkRecovering quasi-real occlusion-free textures for facade models by exploiting fusion of image and laser street data and image inpainting / Karim Hammoudi (2012)PermalinkRemote sensing training in scientific master in France and Africa: Educational choices according to students' academic initial training and first distance learning feedback in Cameroon / Jean-Paul Rudant (2012)Permalink2D change detection from satellite imagery: Performance analysis and impact of the spatial resolution of input images / Nicolas Champion (2011)Permalink