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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5600812 Titre : A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Xin Huang, Auteur ; Yujin Feng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5600812 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] carte de profondeur [Termes IGN] déformation d'objet [Termes IGN] effet de profondeur cinétique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image aérienne [Termes IGN] jeu de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle stéréoscopique [Termes IGN] reconstruction d'image [Termes IGN] réseau neuronal profond [Termes IGN] segmentation sémantique Résumé : (auteur) Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset ( https://irsip.whu.edu.cn/resources/resources_en_v2.php ), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark. Numéro de notice : A2023-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234694 En ligne : https://doi.org/10.1109/TGRS.2023.3234694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102488 [article]

Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 598 Titre : Geographic named entity recognition by employing natural language processing and an improved BERT model Type de document : Article/Communication Auteurs : Liufeng Tao, Auteur ; Zhong Xie, Auteur ; Dexin Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données issues des réseaux sociaux [Termes IGN] données publiques [Termes IGN] jeu de données [Termes IGN] reconnaissance de caractères [Termes IGN] reconnaissance de noms [Termes IGN] test de performance [Termes IGN] toponyme [Termes IGN] traitement du langage naturel Résumé : (auteur) Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models. Numéro de notice : A2022-945 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120598 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.3390/ijgi11120598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102178 [article]

3D target detection using dual domain attention and SIFT operator in indoor scenes / Hanshuo Zhao in The Visual Computer, vol 38 n° 11 (November 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 11 (November 2022) . - pp3765 - 3774 Titre : 3D target detection using dual domain attention and SIFT operator in indoor scenes Type de document : Article/Communication Auteurs : Hanshuo Zhao, Auteur ; Dedong Yang, Auteur ; Jiankang Yu, Auteur Année de publication : 2022 Article en page(s) : pp3765 - 3774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection d'objet [Termes IGN] détection de cible [Termes IGN] jeu de données [Termes IGN] objet 3D [Termes IGN] scène intérieure [Termes IGN] SIFT (algorithme) Résumé : (auteur) In a large number of real-life scenes and practical applications, 3D object detection is playing an increasingly important role. We need to estimate the position and direction of the 3D object in the real scene to complete the 3D object detection task. In this paper, we propose a new network architecture based on VoteNet to detect 3D point cloud targets. On the one hand, we use channel and spatial dual-domain attention module to enhance the features of the object to be detected while suppressing other useless features. On the other hand, the SIFT operator has scale invariance and the ability to resist occlusion and background interference. The PointSIFT module we use can capture information in different directions of point cloud in space, and is robust to shapes of different proportions, so as to better detect objects that are partially occluded. Our method is evaluated on the SUN-RGBD and ScanNet datasets of indoor scenes. The experimental results show that our method has better performance than VoteNet. Numéro de notice : A2022-840 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02217-z Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02217-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102042 [article]

Foreground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 11 (November 2022) . - pp 731 - 738 Titre : Foreground-aware refinement network for building extraction from remote sensing images Type de document : Article/Communication Auteurs : Zhang Yan, Auteur ; Wang Xiangyu, Auteur ; Zhang Zhongwei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 731 - 738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de régions [Termes IGN] détection du bâti [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] jeu de données Résumé : (auteur) To extract buildings accurately, we propose a foreground-aware refinement network for building extraction. In particular, in order to reduce the false positive of buildings, we design the foreground-aware module using the attention gate block, which effectively suppresses the features of nonbuilding and enhances the sensitivity of the model to buildings. In addition, we introduce the reverse attention mechanism in the detail refinement module. Specifically, this module guides the network to learn to supplement the missing details of the buildings by erasing the currently predicted regions of buildings and achieves more accurate and complete building extraction. To further optimize the network, we design hybrid loss, which combines BCE loss and SSIM loss, to supervise network learning from both pixel and structure layers. Experimental results demonstrate the superiority of our network over state-of-the-art methods in terms of both quantitative metrics and visual quality. Numéro de notice : A2022-842 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00081R2 Date de publication en ligne : 01/11/2022 En ligne : https://doi.org/10.14358/PERS.21-00081R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102055 [article]

Exemplaires(1)

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105-2022111	SL	Revue	Centre de documentation	Revues en salle	Disponible

GA-Net: A geometry prior assisted neural network for road extraction / Xin Chen in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103004 Titre : GA-Net: A geometry prior assisted neural network for road extraction Type de document : Article/Communication Auteurs : Xin Chen, Auteur ; Qun Sun, Auteur ; Wenyue Guo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103004 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] données multiéchelles [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction du réseau routier [Termes IGN] jeu de données [Termes IGN] Massachusetts (Etats-Unis) Résumé : (auteur) With geospatial intelligence research developing rapidly, automatic road extraction is becoming a fundamental and challenging task. Due to the special geometric structure and spectral information of road networks, existing methods suffer from incomplete and fractured results. In this work, a novel road extraction convolutional neural network, incorporating the road boundary details and road junction information via a dual-branch multi-task structure, is proposed to learn synergistic feature representations and strengthen road connectivity. Firstly, a BiFPN-based feature aggregation module is utilised to bridge the semantic gap between low-level and high-level feature maps, allowing multi-scale spatial details to be fully fused. Secondly, the boundary auxiliary branch, using a U-shaped network with a spatial-channel attention module, captures residential information for the backbone to enhance the subtleties of road edges. Thirdly, the node inferring branch models the road junction position jointly with the road surface, aiming to strengthen the topology structure and reduce the fragmented road segments. We perform experiments on three diverse road datasets, namely the DeepGlobe dataset, Massachusetts dataset, and SpaceNet dataset. The results demonstrate that our model shows an overall performance improvement over some SOTA algorithms and the IoU indicator achieves 3.86%, 0.79%, and 1.71% improvements over Unet on the three datasets, respectively. Numéro de notice : A2022-785 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103004 En ligne : https://doi.org/10.1016/j.jag.2022.103004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101888 [article]

Comparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)  

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Location-aware neural graph collaborative filtering / Shengwen Li in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

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Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images / Zhiyong Lv in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

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A lightweight network with attention decoder for real-time semantic segmentation / Kang Wang in The Visual Computer, vol 38 n° 7 (July 2022)  

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Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

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A cost-effective algorithm for calibrating multiscale geographically weighted regression models / Bo Wu in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

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Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)  

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A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)  

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Development of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)

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Evaluation de méthodes automatisées de cartographie des zones inondables adaptées à la prévision des crues soudaines / Nabil Hocini (2022)  

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