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Topographic descriptors on the early Dutch charts of the antipodes / Jan Tent in International journal of cartography, vol 8 n° 3 (November 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 3 (November 2022) . - pp 272 - 290 Titre : Topographic descriptors on the early Dutch charts of the antipodes Type de document : Article/Communication Auteurs : Jan Tent, Auteur Année de publication : 2022 Article en page(s) : pp 272 - 290 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes IGN] Australie [Termes IGN] carte ancienne [Termes IGN] descripteur [Termes IGN] explorateur [Termes IGN] littoral [Termes IGN] néerlandais (langue) [Termes IGN] nomenclature [Termes IGN] Nouvelle-Zélande [Termes IGN] Papouasie-Nouvelle-Guinée [Termes IGN] toponyme Résumé : (auteur) The early Dutch charts of coastal Australia, New Zealand and New Guinea are peppered not only with toponyms but also with topographic descriptors. The latter were intended as navigational aids and warnings for future navigators. Naming or describing a geographic feature is a method of distinguishing it from the surrounding topography. At times some topographic descriptors have been considered or interpreted as toponyms. This article explores whether there are any means of determining the difference between the two, and what may have been initially intended by the explorers who entered them on their manuscript charts. Reasons for the relevance of making such a distinction are also considered. Numéro de notice : A2022-746 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1859937 Date de publication en ligne : 11/02/2021 En ligne : https://doi.org/10.1080/23729333.2020.1859937 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101731 [article]

Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 313 - 337 Titre : Polyline simplification based on the artificial neural network with constraints of generalization knowledge Type de document : Article/Communication Auteurs : Jiawei Du, Auteur ; Jichong Yin, Auteur ; Chengyi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 313 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] descripteur [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] généralisation cartographique automatisée [Termes IGN] polyligne [Termes IGN] programmation par contraintes [Termes IGN] réseau neuronal artificiel [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles. Numéro de notice : A2022-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2021.2013944 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100885 [article]

Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821 Titre : Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Zhipeng Gui, Auteur ; Jimin Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 799 - 821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] descripteur [Termes IGN] données d'entrainement sans étiquette [Termes IGN] image cartographique [Termes IGN] métadonnées [Termes IGN] projection [Termes IGN] système d'information géographique [Termes IGN] Web Map Service [Termes IGN] web mapping Résumé : (auteur) Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach. Numéro de notice : A2022-258 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1968407 En ligne : https://doi.org/10.1080/13658816.2021.1968407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100231 [article]

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 géométrique [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

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

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Deep learning for feature based image matching / Lin Chen (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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Semantic relatedness algorithm for keyword sets of geographic metadata / Zugang Chen in Cartography and Geographic Information Science, vol 47 n° 2 (February 2020)  

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Cartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)  

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Cross-year multi-modal image retrieval using siamese networks / Margarita Khokhlova (2020)  

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SUMAC'20 : Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2020)  

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Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

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Learning scene geometry for visual localization in challenging conditions / Nathan Piasco (2019)    

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Can a machine generate humanlike language descriptions for a remote sensing image? / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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