Publications du LaSTIG
Les publications antérieures au LaSTIG sont celles des laboratoires qui ont formé le LaSTIG : COGIT, LOEMI et MATIS, à l'exception du LAREG - Vous pouvez affiner la recherche au sein des références
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Generative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)
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Titre : Generative adversarial networks to generalise urban areas in topographic maps Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya
, Auteur ; Xiang Zhang, 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-B4-2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 4 Importance : pp 15 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] carte topographique
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas. We use as input detailed buildings, roads, and rivers from topographic datasets produced by the French national mapping agency (IGN), and we expect as output of the GAN a legible map of these elements at a target scale of 1:50,000. This level of detail requires to reduce the amount of information while preserving patterns; covering dense inner cities block by a unique polygon is also necessary because these blocks cannot be represented with enlarged individual buildings. The target map has a style similar to the topographic map produced by IGN. This experiment succeeded in producing image tiles that look like legible maps. It also highlights the impact of data and representation choices on the quality of predicted images, and the challenge of learning geographic relationships. Numéro de notice : C2021-016 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B4-2021-15-2021 Date de publication en ligne : 30/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-15-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98062 How do users interact with Virtual Geographic Environments? Users’ behavior evaluation in urban participatory planning / Thibaud Chassin (2021)
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Titre : How do users interact with Virtual Geographic Environments? Users’ behavior evaluation in urban participatory planning Type de document : Article/Communication Auteurs : Thibaud Chassin, Auteur ; Jens Ingensand, Auteur ; Guillaume Touya , Auteur ; Sidonie Christophe
, Auteur
Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Projets : 3-projet - voir note / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie OA Archives Commission 4 Note générale : bibliographie
This study was partly funded by the Computers & Geosciences Research Scholarships co-sponsored by Elsevier and the International Association for Mathematical Geosciences (IAMG). The in-house code used in this study is under MIT licence available on github: https://github.com/thibaud-c/3DperceptionUX. The VGEs are published on Zenedo, doi: 10.5281/zenodo.5137307.Langues : Anglais (eng) Descripteur : [Termes IGN] approche participative
[Termes IGN] comportement
[Termes IGN] données localisées 3D
[Termes IGN] environnement géographique virtuel
[Termes IGN] planification urbaine
[Termes IGN] utilisateur civil
[Termes IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) For the past twenty years, the adoption of Virtual Geographic Environments is thriving. This democratization is due to numerous new opportunities offered by this medium. However, in participatory urban planning these interactive 3D geovisualizations are still labeled as very advanced means, and are only scarcely used. The involvement of citizens in urban decision-making is indeed carefully planned ahead to limit off-topic feedback. A better comprehension of Virtual Geographic Environments, and more specifically of users’ strategic behaviors while interacting with this medium could enhance participants’ contributions. The users’ strategic behavior was assessed in this article through an experimental study. A total of 107 participants completed online tasks about the identification of 3D scenes’ footprints, the comparison of buildings’ heights, and the visibility of objects through the scenes. The interactions of the participants were recorded (i.e. pressed keys, pointing device interactions), as well as the camera positions adopted to complete specific tasks. The results show that: (1) users get more efficient throughout the study; (2) interruptions in 3D manipulation appear to highlight difficulties in interacting with the virtual environments; (3) users tend to centralize their positions within the scene, notably around their starting position; (4) the type of task strongly affects the behavior of users, limiting or broadening their explorations. The results of this experimental study are a valuable resource that can be used to improve the design of future urban planning projects involving Virtual Geographic Environments, e.g. with the creation of personalized 3D tools. Numéro de notice : C2021-047 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-4-19-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-19-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99395
contenu dans SUMAC'21: Proceedings of the 3rd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2021)
Titre : How to spatialize geographical iconographic heritage Type de document : Article/Communication Auteurs : Emile Blettery , Auteur ; Nelson Fernandes, Auteur ; Valérie Gouet-Brunet
, Auteur
Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SUMAC 2021, 3rd workshop on Structuring and Understanding of Multimedia heritAge Contents 20/10/2021 24/10/2021 Chengdu Chine OA Archives Commission 4 Importance : 10 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'images
[Termes IGN] estimation de pose
[Termes IGN] géolocalisation
[Termes IGN] géoréférencement indirect
[Termes IGN] image ancienne
[Termes IGN] image numérisée
[Termes IGN] patrimoine culturel
[Termes IGN] photographie aérienne oblique
[Termes IGN] photographie terrestre
[Termes IGN] recherche d'image basée sur le contenuRésumé : (auteur) This article is dedicated to the spatialization of image contents, with a focus on geographical iconographic heritage, i.e. digitized or born-digital image collections, acquired at variable temporal periods and showing the territory and its human-made and natural visual landmarks. We present a panorama of the current solutions (manual, semi-automatic and fully automatic alternatives) that exist to spatialize a visual content, with respect to the data available and the level of spatialization targeted. In particular, we highlight the characteristics of the approaches dedicated to geographical iconographic heritage, and in some cases, we present tests and practical feedbacks that we had the opportunity to conduct for old photographic contents in oblique aerial and terrestrial imagery. Numéro de notice : C2021-035 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3475720.3484444 En ligne : https://doi.org/10.1145/3475720.3484444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99054 Documents numériques
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Titre : ICDAR 2021 competition on historical map segmentation Type de document : Article/Communication Auteurs : Joseph Chazalon, Auteur ; Edwin Carlinet, Auteur ; Yizi Chen , Auteur ; Julien Perret
, Auteur ; Bertrand Duménieu
, Auteur ; Clément Mallet
, Auteur ; Thierry Géraud, Auteur ; Vincent Nguyen, Auteur ; Nam Nguyen, Auteur ; Josef Baloun, Auteur ; Ladislav Lenc, Auteur ; Pavel Král, Auteur
Editeur : Le Kremlin Bicêtre : Ecole pour l'Informatique et les Techniques Avancées EPITA Année de publication : 2021 Projets : 1-Pas de projet / Gouet-Brunet, Valérie Conférence : ICDAR 2021, 16th International Conference on Document Analysis and Recognition 05/09/2021 10/09/2021 Lausanne Suisse OA Archives Commission 4 Importance : 15 p. Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task 2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task 3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks 2 and 3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at https://icdar21-mapseg.github.io/. Numéro de notice : C2021-022 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-03256193 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98032 Improving image description with auxiliary modality for visual localization in challenging conditions / Nathan Piasco in International journal of computer vision, vol 29 n° 1 (January 2021)
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[article]
Titre : Improving image description with auxiliary modality for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Valérie Gouet-Brunet
, Auteur ; Cédric Demonceaux, Auteur
Année de publication : 2021 Projets : PLaTINUM / Gouet-Brunet, Valérie Article en page(s) : pp 185 - 202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] descripteur
[Termes IGN] localisation basée image
[Termes IGN] localisation basée visionRésumé : (auteur) Image indexing for lifelong localization is a key component for a large panel of applications, including robot navigation, autonomous driving or cultural heritage valorization. The principal difficulty in long-term localization arises from the dynamic changes that affect outdoor environments. In this work, we propose a new approach for outdoor large scale image-based localization that can deal with challenging scenarios like cross-season, cross-weather and day/night localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We show through extensive evaluation that our method can improve localization performances, especially in challenging scenarios when the visual appearance of the scene has changed. Our method is able to leverage both visual and geometric clues from monocular images to create discriminative descriptors for cross-season localization and effective matching of images acquired at different time periods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Finally we extended our method to reflectance modality and we compare multi-modal descriptors respectively based on geometry, material reflectance and a combination of both. Numéro de notice : A2021-132 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-020-01363-6 Date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1007/s11263-020-01363-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96971
in International journal of computer vision > vol 29 n° 1 (January 2021) . - pp 185 - 202[article] PermalinkPermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)
PermalinkLearning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)
PermalinkLearning to translate land-cover maps: Several multi-dimensional context-wise solutions / Luc Baudoux (2021)
PermalinkPermalinkLeveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)
PermalinkPermalinkMapping and characterizing animals’ places of interest in forest environment / Laurence Jolivet (2021)
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