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scientifique

Marrying 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)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 101 - 107 Titre : Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images Type de document : Article/Communication Auteurs : Guillemette Fonteix, Auteur ; M. Swaine, Auteur ; M. Leras, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 107 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] carte de confiance [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] fusion d'images [Termes IGN] image optique [Termes IGN] image Sentinel-MSI [Termes IGN] segmentation sémantique [Termes IGN] série temporelle Résumé : (auteur) The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image. Numéro de notice : A2021-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2021-101-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-3-2021-101-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97957 [article]

Scalable 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)  

Public [article]  inProceedings of the National Academy of Sciences of the United States of America PNAS > vol 118 n° 17 (27 April 2021) . - n° e2018863118 Titre : Scalable deep learning to identify brick kilns and aid regulatory capacity Type de document : Article/Communication Auteurs : Jihyeon Lee, Auteur ; Nina R. Brooks, Auteur ; Fahim Tajwar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° e2018863118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Bangladesh [Termes IGN] chaîne de traitement [Termes IGN] image Worldview [Termes IGN] pollution atmosphérique Mots-clés libres : briqueterie Résumé : (auteur) Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate—a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 μg/m3 of PM2.5 (particulate matter of a diameter less than 2.5 μm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry. Numéro de notice : A2021-793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1073/pnas.2018863118 En ligne : https://doi.org/10.1073/pnas.2018863118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99084 [article]

A 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)  

Public [article]  inAnnals of GIS > vol 27 n° 2 (April 2021) . - pp 211 - 224 Titre : A user-driven process for INSPIRE-compliant land use database: example from Wallonia, Belgium Type de document : Article/Communication Auteurs : Benjamin Beaumont, Auteur ; Tais Grippa, Auteur ; Moritz Lennert, Auteur Année de publication : 2021 Article en page(s) : pp 211 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] aménagement du territoire [Termes IGN] base de données foncières [Termes IGN] carte d'occupation du sol [Termes IGN] chaîne de traitement [Termes IGN] données massives [Termes IGN] édition en libre accès [Termes IGN] infrastructure européenne de données localisées [Termes IGN] INSPIRE [Termes IGN] utilisateur [Termes IGN] utilisation du sol [Termes IGN] Wallonie (Belgique) Résumé : (auteur) Regional land use monitoring at high spatial, temporal, and thematic resolution is an important expectation of Walloon stakeholders. Over the last decade, increased data-processing capacities and the annual acquisition of remotely sensed data have resulted in the production of a large amount of relevant geodata. The INSPIRE directive and its obligations for 2020 serve as a path for the development of a new user-driven and open-source hierarchical land use classification system mapping scheme, as presented in this paper. The process includes intensive user consultation, the development of an entire automatic processing chain, and efforts to address challenges such as big data handling, the variability of input data properties, and reproducibility. The thematically detailed land use map, with its 69 classes, is already widely used by Walloon stakeholders, and new demands for updating have already emerged. Based on a European classification system that is compulsory for all member states, INSPIRE-compliant land use maps will make it possible to carry out cross-border studies and compare spatial planning strategies between states. Numéro de notice : A2021-626 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2021.1875047 Date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1080/19475683.2021.1875047 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98256 [article]

Passive 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2627 - 2742 Titre : Passive radar imaging of ship targets with GNSS signals of opportunity Type de document : Article/Communication Auteurs : Debora Pastina, Auteur ; Fabrizio Santi, Auteur ; Federica Pieralice, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2627 - 2742 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] capteur passif [Termes IGN] chaîne de traitement [Termes IGN] détection de cible [Termes IGN] extraction de traits caractéristiques [Termes IGN] image radar [Termes IGN] navigation maritime [Termes IGN] navire [Termes IGN] objet mobile [Termes IGN] radar bistatique [Termes IGN] signal GNSS [Termes IGN] télédétection spatiale Résumé : (Auteur) This article explores the possibility to exploit global navigation satellite systems (GNSS) signals to obtain radar imagery of ships. This is a new application area for the GNSS remote sensing, which adds to a rich line of research about the alternative utilization of navigation satellites for remote sensing purposes, which currently includes reflectometry, passive radar, and synthetic aperture radar (SAR) systems. In the field of short-range maritime surveillance, GNSS-based passive radar has already proven to detect and localize ship targets of interest. The possibility to obtain meaningful radar images of observed vessels would represent an additional benefit, opening the doors to noncooperative ship classification capability with this technology. To this purpose, a proper processing chain is here conceived and developed, able to achieve well-focused images of ships while maximizing their signal-to-background ratio. Moreover, the scaling factors needed to map the backscatter energy in the range and cross-range domain are also analytically derived, enabling the estimation of the length of the target. The effectiveness of the proposed approach at obtaining radar images of ship targets and extracting relevant features is confirmed via an experimental campaign, comprising multiple Galileo satellites and a commercial ferry undergoing different kinds of motion. Numéro de notice : A2021-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3005306 Date de publication en ligne : 16/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3005306 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97210 [article]

AI4GEO: a data intelligence platform for 3D geospatial mapping / Pierre-Marie Brunet (2021)  

Public Titre : AI4GEO: a data intelligence platform for 3D geospatial mapping Type de document : Article/Communication Auteurs : Pierre-Marie Brunet, Auteur ; Pierre Lassalle, Auteur ; Simon Baillarin, Auteur ; Bruno Vallet , Auteur ; Arnaud Le Bris , Auteur ; Gaëlle Romeyer , Auteur ; Guy Le Besnerais, Auteur ; Flora Weissgerber, Auteur ; Gilles Foulon, Auteur ; Vincent Gaudissart, Auteur ; Christophe Triquet, Auteur ; Michael Darques, Auteur ; Gwénaël Souillé, Auteur ; Laurent Gabet, Auteur ; Cedrik Ferrero, Auteur ; Thanh-Long Huynh, Auteur ; Emeric Lavergne, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Projets : AI4GEO / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 817 - 823 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] chaîne de traitement [Termes IGN] données localisées 3D [Termes IGN] données massives [Termes IGN] jeu de données localisées [Termes IGN] plateforme logicielle [Termes IGN] segmentation sémantique [Termes IGN] traitement de données localisées Résumé : (auteur) The availability of 3D Geospatial information is a key issue for many expanding sectors such as autonomous vehicles, business intelligence and urban planning. Its production is now possible thanks to the abundance of available data (Earth observation satellite constellations, insitu data, …) but manual interventions are still needed to guarantee a high level of quality, which prevents mass production. New artificial intelligence and big data technologies adapted to 3D imagery can help to remove these obstacles. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information and new added-value services. This paper will first introduce AI4GEO initiative, context and overall objectives. It will then present the current status of the project and in particular it will focus on the innovative platform put in place to handle big 3D datasets for analytics needs and it will present the first results of 3D semantic segmentations and associated perspectives. Numéro de notice : C2021-015 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-817-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-817-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98067

Automatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)  

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Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)  

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Conception d’un outil de visualisation 3D et de visite virtuelle orienté web / Elisa Lembert (2021)  

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Deep learning for feature based image matching / Lin Chen (2021)  

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Dynamic scene understanding using deep neural networks / Ye Lyu (2021)  

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From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)  

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Geometric approximation of structured scenes from images / Muxingzi Li (2021)  

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Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)  

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Intégration et analyse de données massives et hétérogènes pour une observation intelligente du territoire / Rodrigue Kafando (2021)  

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Programmation 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) 

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