Centre dedocumentation
scientifique

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. XLIII-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- ) 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

Flex-ER: A platform to evaluate interaction techniques for immersive visualizations / María-Jesús Lobo in Proceedings of the ACM on Human-Computer Interaction, Vol 4 (November 2020)  

Public [article]  inProceedings of the ACM on Human-Computer Interaction > Vol 4 (November 2020) . - n° 195 Titre : Flex-ER: A platform to evaluate interaction techniques for immersive visualizations Type de document : Article/Communication Auteurs : María-Jesús Lobo , Auteur ; Christophe Hurter, Auteur ; Pourang Polad Irani, Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 195 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] environnement de développement [Termes IGN] format JSON [Termes IGN] plateforme logicielle [Termes IGN] visualisation de données [Termes IGN] vue immersive [Vedettes matières IGN] Géovisualisation Mots-clés libres : Flex-ER Résumé : (auteur) Extended Reality (XR) systems (which encapsulate AR, VR and MR) is an emerging field which enables the development of novel visualization and interaction techniques. To develop and to assess such techniques, researchers and designers have to face choices in terms of which development tools to adopt, and with very little information about how such tools support some of the very basic tasks for information visualization, such as selecting data items, linking and navigating. As a solution, we propose Flex-ER, a flexible web-based environment that enables users to prototype, debug and share experimental conditions and results. Flex-ER enables users to quickly switch between hardware platforms and input modalities by using a JSON specification that supports both defining interaction techniques and tasks at a low cost. We demonstrate the flexibility of the environment through three task design examples: brushing, linking and navigating. A qualitative user study suggest that Flex-ER can be helpful to prototype and explore different interaction techniques for immersive analytics. Numéro de notice : A2020-818 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1145/3427323 En ligne : https://doi.org/10.1145/3427323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97091 [article]

Enhancing knowledge, skills, and spatial reasoning through location-based mobile learning / Christian Sailer (2020)  

Public Titre : Enhancing knowledge, skills, and spatial reasoning through location-based mobile learning Type de document : Thèse/HDR Auteurs : Christian Sailer, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2020 Collection : Dissertationen ETH num. 26817 Importance : 289 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse géovisuelle [Termes IGN] apprentissage (cognition) [Termes IGN] enseignement secondaire [Termes IGN] plateforme logicielle [Termes IGN] programme interactif [Termes IGN] raisonnement spatial Résumé : (auteur) Location-based mobile learning (LBML) involves learning in and about locations in order to explore, analyze, describe, and evaluate phenomena in authentic learning experiences and incorporate them into the real world. Learners gain an understanding of boththe immediate environment using all the senses and the spatial perception to create a holistic impression of the environmental phenomena. Mobile mapping technologies and location-based services are utilized to link learning contents to specific locations as stories, tasks, or assignments. A large number of research projects on mobile learning have investigated LBML systems and LBML approaches with regard to learner satisfaction, usability and efficiency. The projects achieved predominantly positive results regarding motivation and engagement but only fragmented results regarding the cognitive learning outcomes.This dissertation describes how an LBML system utilizing GIS technology enhances the educational learning outcomes with a special focus on the spatial thinking process. Furthermore, this dissertation describes novel approaches of visual analytics with 2D and 3D map web components to produce new teaching strategies during the activities and new metacognitive strategies to evaluate and reflect the activity. The study presented in this dissertation covers case studies in universities, vocational schools, and informal education environments using design-based research to develop a mobile-friendly interactive mapping platform. Furthermore, the platform includes multimedia capabilities and interfaces to connect external services and content. The main study was conducted in a secondary school under real conditions and evaluated the technology regarding the learning performance and the teaching activities before, during, and after the activity. The results reveal a better cognitive learning outcome in classroom exams when a teaching sequence of several weeks includes an outdoor activity of a double lesson. Moreover, there is potential for enhancing learning beyond the outdoor part to improve spatial reasoning. Long-term self-assessment of the learners, however, resulted in no impact, whether cognitively or affectively. The workload for outdoor teaching compared to classroom teaching is higher mainly due to the profound inspections of the location. The findings and their implications for research and teacher education were discussed in order to corroborate the educational value of LBML to motivate educators using LBML strategies for teaching. Numéro de notice : 17657 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère En ligne : http://dx.doi.org/10.3929/ethz-b-000458558 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97924

RoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 435 - 443 Titre : RoofN3D: a database for 3D building reconstruction with deep learning Type de document : Article/Communication Auteurs : Andreas Wichmann, Auteur ; Amgad Agoub, Auteur ; Valentina Schmidt, Auteur Année de publication : 2019 Article en page(s) : pp 435 - 443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] .Net [Termes IGN] apprentissage profond [Termes IGN] base de données localisées 3D [Termes IGN] données d'apprentissage [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) Machine learning methods, in particular those based on deep learning, have gained in importance through the latest development of artificial intelligence and computer hardware. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a three-dimensional (3D) point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. The details about RoofN3D and the developed framework to automatically derive such training data are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to 3D point cloud data are presented. Finally, we exemplarily demonstrate how the provided data can be used to classify building roofs with the PointNet framework. Numéro de notice : A2019-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.435 En ligne : https://doi.org/10.14358/PERS.85.6.435 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93004 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019061	SL	Revue	Centre de documentation	Revues en salle	Disponible

CartAGen: an open source research platform for map generalization / Guillaume Touya (2019)    

Public Titre : CartAGen: an open source research platform for map generalization Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur ; Cécile Duchêne , Auteur Editeur : Paris, ... [Suède] : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2019 Autre Editeur : Göttingen : Copernicus publications Collection : Proceedings of the ICA Projets : 1-Pas de projet / Conférence : ICC 2019, 29th International Cartographic Conference ICA, Mapping everything for everyone 15/07/2019 20/07/2019 Tokyo Japon Open Access Proceedings of the ICA Importance : 9 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] CartAGen (plateforme de généralisation) [Termes IGN] généralisation cartographique automatisée [Termes IGN] logiciel libre [Termes IGN] organisme cartographique national [Termes IGN] plateforme logicielle [Termes IGN] système multi-agents [Vedettes matières IGN] Généralisation Résumé : (Auteur) Automatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen. Numéro de notice : C2019-010 Affiliation des auteurs : LaSTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-2-134-2019 date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.5194/ica-proc-2-134-2019 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93258

Documents numériques

peut être téléchargé CartAGen: an open source research platform - pdf éditeur Adobe Acrobat PDF

Retour d'expérience de l'école OpenMOLE "ExModelo", organisée en partenariat avec le méso-centre du CRIANN / Mathieu Leclaire (2019)  

Permalink

Culture 3D cloud: A cloud computing platform for 3D scanning, documentation, preservation and dissemination of cultural heritage / Pierre Alliez in ERCIM News, n° 111 (October 2017) 

Permalink

SCOPED-W : SCalable Online Platform for extracting Environmental Data and Water-related model outputs / Yaniss Guigoz in Transactions in GIS, vol 21 n° 4 (August 2017)  

Permalink

EuroSDR annual report 2016 / European Spatial Data Research EuroSDR (2017)  

Permalink

iTowns : an open source project connected to a research platform for continuous innovation / Alexandre Devaux (2017) 

Permalink

ESPON FIT: functional indicators for spatial-aware policy-making / Hadrien Commenges in Cartographica, vol 51 n° 3 (Fall 2016)  

Permalink

Spatial discovery and the research library / Sara Lafia in Transactions in GIS, vol 20 n° 3 (June 2016)  

Permalink

Service-oriented model-encapsulation strategy for sharing and integrating heterogeneous geo-analysis models in an open web environment / Songshan Yue in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)  

Permalink

Unlock the value of network data / Michael Baker in GEO: Geoconnexion international, vol 15 n° 4 (April 2016)

Permalink

GéoWorld 2015-2016 : le plein de nouveauté / Anonyme in Géomatique expert, n° 109 (mars - avril 2016)

Permalink