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Learning indoor point cloud semantic segmentation from image-level labels / Youcheng Song in The Visual Computer, vol 38 n° 9 (September 2022)
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[article]
Titre : Learning indoor point cloud semantic segmentation from image-level labels Type de document : Article/Communication Auteurs : Youcheng Song, Auteur ; Zhengxing Sun, Auteur ; Qian Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3253 - 3265 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] apprentissage dirigé
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] image RVB
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) The data-hungry nature of deep learning and the high cost of annotating point-level labels make it difficult to apply semantic segmentation methods to indoor point cloud scenes. Therefore, exploring how to make point cloud segmentation methods less rely on point-level labels is a promising research topic. In this paper, we introduce a weakly supervised framework for semantic segmentation on indoor point clouds. To reduce the labor cost in data annotation, we use image-level weak labels that only indicate the classes that appeared in the rendered images of point clouds. The experiments validate the effectiveness and scalability of our framework. Our segmentation results on both ScanNet and S3DIS datasets outperform the state-of-the-art method using a similar level of weak supervision. Numéro de notice : A2022-793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-022-02569-0 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1007/s00371-022-02569-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101917
in The Visual Computer > vol 38 n° 9 (September 2022) . - pp 3253 - 3265[article]Invariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)
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Titre : Invariant structure representation for remote sensing object detection based on graph modeling Type de document : Article/Communication Auteurs : Zicong Zhu, Auteur ; Xian Sun, Auteur ; Wenhui Diao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5625217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] granularité d'image
[Termes IGN] graphe
[Termes IGN] invariantRésumé : (auteur) Due to the characteristics of vertical orthophoto imaging, the apparent structural features of the object in the remote sensing (RS) image are relatively stable, such as the cross-shaped structure of the aircraft and the rectangular structure of the vehicle. Compared with the traditional visual features, using these features is conducive to improving the accuracy of object detection. However, there are few studies on such characteristics. In this article, we systematically study the invariant structural features of remote sensing objects and propose a graph focusing aggregation network (GFA-Net) to represent the structural features of remote sensing objects. Among them, in view of the problem that traditional convolutional neural networks (CNNs) are sensitive to the changes in rotation, scale, and other factors, which makes it difficult to extract structural features, we propose the graph focusing process (GFP) based on the idea of graph convolution. Analysis and experiments show that graph structure has significant advantages over Euclidean feature space under CNN in expressing such structural features. In order to realize the end-to-end efficient training of the above model, we design a graph aggregation network (GAN) to update the weight of nodes. We verify the effectiveness of our method on the proposed multitask datasets aircraft component segmentation dataset (ACSD) and the large-scale Fine-grAined object recognItion in high-Resolution RS imagery (FAIR1M). Experiments conducted on the object detection datasets of large-scale Dataset for Object deTection in Aerial images (DOTA) and HRSC2016 prove that the proposed method is superior to the current state-of-the-art (SOTA) method. Numéro de notice : A2022-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3181686 Date de publication en ligne : 09/06/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3181686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101186
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 5625217[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)
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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 mappingRésumé : (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
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821[article]Global and climate challenges, graph-based data analysis for multisource information extraction / Morgane Batelier (2022)
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Titre : Global and climate challenges, graph-based data analysis for multisource information extraction Type de document : Mémoire Auteurs : Morgane Batelier, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 43 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de fin d'études, cycle des ingénieurs ENSG 3ème année, FRSLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Arctique, océan
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] glace de mer
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-SAR
[Termes IGN] polarimétrie radar
[Termes IGN] traitement d'image radarIndex. décimale : MPT Mémoires de fin d'études du Master Méthodes physiques en télédétection Résumé : (Auteur) During my end-of-studies internship, I worked on the development of a label propagation algorithm for remote sensing data, using Deep Learning. It was mainly applied to sea ice classification using SAR Sentinel-1 data, and to hyperspectral imaging in order to be effective to multimodal remote sensing. I started by the bibliography, during which we decided with my supervisors the method I was going to work from. Then, I worked on the algorithm implementation that was the longest phase. Finally, the last part of my work was the certification and improvement of the results using different process. Note de contenu : Introduction
1. Remote Sensing in the Arctic
1.1 Challenges of the Arctic
1.2 Sea Ice
2. Label Propagation for Deep Learning
2.1 Preliminaries
2.2 Transductive Propagation Network for Few-shot Learning
3. Multimodal Remote Sensing Data
3.1 Synthetic Aperture Radar
3.2 Hyperspectral Imaging
4. Experimental results
4.1 Datasets
4.2 Improvement Methods
4.3 Discussion and future of the algorithm
ConclusionNuméro de notice : 26935 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Center for Integrated Remote Sensing and Forecasting for Arctic Operations CIRFA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102059 Documents numériques
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Global and climate challenges, graph-based data analysis for multisource information extraction - pdf auteurAdobe Acrobat PDFConstrained shortest path problems in bi-colored graphs: a label-setting approach / Amin AliAbdi in Geoinformatica, vol 25 n° 3 (July 2021)
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Titre : Constrained shortest path problems in bi-colored graphs: a label-setting approach Type de document : Article/Communication Auteurs : Amin AliAbdi, Auteur ; Ali Mohades, Auteur ; Mansoor Davoodi, Auteur Année de publication : 2021 Article en page(s) : pp 513 - 531 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] calcul d'itinéraire
[Termes IGN] chemin le plus court, algorithme du
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] graphe
[Termes IGN] programmation par contraintesRésumé : (auteur) Definition of an optimal path in the real-world routing problems is not necessarily the shortest one, because parameters such as travel time, safety, quality, and smoothness also played essential roles in the definition of optimality. In this paper, we use bi-colored graphs for modeling urban and heterogeneous environments and introduce variations of constraint routing problems. Bi-colored graphs are a kind of directed graphs whose vertices are divided into two subsets of white and gray. We consider two criteria, minimizing the length and minimizing the number of gray vertices and present two problems called gray vertices bounded shortest path problem and length bounded shortest path problem on bi-colored graphs. We propose an efficient time label-setting algorithm to solve these problems. Likewise, we simulate the algorithm and compare it with the related path planning methods on random graphs as well as real-world environments. The simulation results show the efficiency of the proposed algorithm. Numéro de notice : A2021-974 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00385-8 Date de publication en ligne : 03/12/2019 En ligne : https://doi.org/10.1007/s10707-019-00385-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100393
in Geoinformatica > vol 25 n° 3 (July 2021) . - pp 513 - 531[article] PermalinkConférence d'apprentissage 99, actes de CAP'99, Ecole Polytechnique, Palaiseau, 15-18 juin 1999 / Michèle Sebag (1999)
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