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GisGCN: a visual graph-based framework to match geographical areas through time / Margarita Khokhlova in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 97 Titre : GisGCN: a visual graph-based framework to match geographical areas through time Type de document : Article/Communication Auteurs : Margarita Khokhlova , Auteur ; Nathalie Abadie , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut géomètrique [Termes IGN] attribut sémantique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données étiquetées d'entrainement [Termes IGN] entité géographique [Termes IGN] image aérienne [Termes IGN] réseau sémantique Résumé : (auteur) Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Numéro de notice : A2022-156 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020097 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.3390/ijgi11020097 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100316 [article]

ALEGORIA: Joint multimodal search and spatial navigation into the geographic iconographic heritage / Florent Geniet (2022)  

Public Titre : ALEGORIA: Joint multimodal search and spatial navigation into the geographic iconographic heritage Type de document : Article/Communication Auteurs : Florent Geniet, Auteur ; Valérie Gouet-Brunet , Auteur ; Mathieu Brédif , Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : MM 2022, 30th ACM International Conference on Multimedia 10/10/2022 14/10/2022 Lisbonne Portugal Proceedings ACM Importance : pp 6982 - 6984 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] géolocalisation [Termes IGN] image ancienne [Termes IGN] moteur de recherche [Termes IGN] photographie aérienne oblique [Termes IGN] plateforme logicielle [Termes IGN] visualisation 3D Résumé : (auteur) In this article, we present two online platforms developed for the structuring and valorization of old geographical iconographic collections: a multimodal search engine for their indexing, retrieval and interlinking, and a 3D navigation platform for their visualization in spatial context. In particular, we show how the joint use of these functionalities, guided by geolocation, brings structure and knowledge to the manipulated collections. In the demonstrator, they consist of 54,000 oblique aerial photographs from several French providers (national archives, a museum and a mapping agency). Numéro de notice : C2022-042 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3503161.3547746 Date de publication en ligne : 10/10/2022 En ligne : https://doi.org/10.1145/3503161.3547746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101906

Automatic structuring of photographic collections for spatio-temporal monitoring of restoration sites: problem statement and challenges / Laura Willot (2022)  

Public Titre : Automatic structuring of photographic collections for spatio-temporal monitoring of restoration sites: problem statement and challenges Type de document : Article/Communication Auteurs : Laura Willot, Auteur ; D. Vodislav, Auteur ; Livio de Luca, Auteur ; Valérie Gouet-Brunet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 46-2-W1 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : 3D-ARCH 2022, 9th International Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures" 02/03/2022 04/03/2022 Mantua Italie OA ISPRS Archives Importance : pp 521 - 528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] cathédrale [Termes IGN] enrichissement sémantique [Termes IGN] image [Termes IGN] mesure de similitude [Termes IGN] modèle conceptuel de données [Termes IGN] Paris (75) [Termes IGN] patrimoine documentaire [Termes IGN] recherche d'image basée sur le contenu Résumé : (auteur) Over the last decade, a large number of digital documentation projects have demonstrated the potential of image-based modelling of heritage objects in the context of documentation, conservation, and restoration. The inclusion of these emerging methods in the daily monitoring of the activities of a heritage restoration site (context in which hundreds of photographs per day can be acquired by multiple actors, in accordance with several observation and analysis needs) raises new questions at the intersection of big data management, analysis, semantic enrichment, and more generally automatic structuring of this data. In this article, we propose a data model developed around these questions and identify the main challenges to overcome the problem of structuring massive collections of photographs through a review of the available literature on similarity metrics used to organise the pictures based on their content or metadata. This work is realized in the context of the restoration site of the Notre-Dame de Paris cathedral that will be used as the main case study. Numéro de notice : C2022-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-xlvi-2-w1- 2022-521-2022 Date de publication en ligne : 25/02/2022 En ligne : https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-2-W1-2022/5 [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100315

Cross-dataset learning for generalizable land use scene classification / Dimitri Gominski (2022)  

Public 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. 1382-1391 Langues : 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 sol Résumé : (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

Location retrieval using qualitative place signatures of visible landmarks / Lijun Wei (2022)  

Public Titre : Location retrieval using qualitative place signatures of visible landmarks Type de document : Article/Communication Auteurs : Lijun Wei , Auteur ; Valérie Gouet-Brunet , Auteur ; Anthony Cohn, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : 1-Pas de projet / Gouet-Brunet, Valérie Importance : 52 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] descripteur [Termes IGN] lieu [Termes IGN] point de repère [Termes IGN] reconnaissance d'objets [Termes IGN] relation spatiale Résumé : (auteur) Location retrieval based on visual information is to retrieve the location of an agent (e.g. human, robot) or the area they see by comparing the observations with a certain form of representation of the environment. Existing methods generally require precise measurement and storage of the observed environment features, which may not always be robust due to the change of season, viewpoint, occlusion, etc. They are also challenging to scale up and may not be applicable for humans due to the lack of measuring/imaging devices. Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a qualitative location retrieval method is proposed in this work by describing locations/places using qualitative place signatures (QPS), defined as the perceived spatial relations between ordered pairs of co-visible landmarks from viewers' perspective. After dividing the space into place cells each with individual signatures attached, a coarse-to-fine location retrieval method is proposed to efficiently identify the possible location(s) of viewers based on their qualitative observations. The usability and effectiveness of the proposed method were evaluated using openly available landmark datasets, together with simulated observations by considering the possible perception error. Numéro de notice : P2022-009 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2208.00783 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.48550/arXiv.2208.00783 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101879

Towards culture-aware smart and sustainable cities: Integrating historical sources in spatial information infrastructures / Bénédicte Bucher in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

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Connecting images through sources: Exploring low-data, heterogeneous instance retrieval / Dimitri Gominski in Remote sensing, vol 13 n° 16 (August-2 2021)  

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Semantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol 80 n° 15 (June 2021)  

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Restituer les bidonvilles de Nanterre : l’apport d’un outil de visualisation 3D à un projet de sciences sociales / Paul Lecat in Humanités numériques, n° 3 (2021)  

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Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)  

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Description et recherche d’image généralisables pour l’interconnexion et l’analyse multi-source / Dimitri Gominski (2021)  

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How to spatialize geographical iconographic heritage / Emile Blettery (2021)    

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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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Intelligent embedded camera for robust object tracking on mobile platform / Imane Salhi (2021)  

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Learning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)  

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