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Évaluation de la qualité de modèles 3D issus de nuages de points / Tania Landes in XYZ, n° 171 (juin 2022)
[article]
Titre : Évaluation de la qualité de modèles 3D issus de nuages de points Type de document : Article/Communication Auteurs : Tania Landes, Auteur Année de publication : 2022 Article en page(s) : pp 14 - 24 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maquette numérique
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité du modèle
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) La modélisation 3D répond à la fois à un enjeu économique, mais aussi environnemental, que ce soit à l’échelle du bâtiment ou de la ville. Ces dix dernières années, les techniques d’acquisition ont considérablement évolué du point de vue de leur rapidité, du volume de données à gérer, de l’hétérogénéité des informations acquises par les systèmes multicapteurs, de même que des méthodes de traitement des données. De nouveaux processus sont nés de ces bouleversements, comme le processus “scan-to-BIM”, caractérisant les étapes menant du nuage de points à une maquette numérique intelligente. En adoptant la maquette numérique, intégrée dans un processus collaboratif BIM (Building Information Modeling), les acteurs du bâtiment sont en mesure d’effectuer des simulations et de réduire, en plus des coûts, l’impact environnemental lié aux interventions sur le bâtiment, tout au long de son cycle de vie. En pratique, pour aboutir à une maquette numérique intelligente du bâtiment à partir d’un relevé de l’existant, de nombreux verrous technologiques sont à lever. Dans ce contexte, j’ai eu l’occasion d’encadrer divers travaux de recherches portant sur les thématiques allant de l’acquisition de données 3D (généralement sous forme de nuages de points 3D) à leur traitement, jusqu’à la production de la maquette numérique comme résumé dans le numéro 167 de la revue XYZ [Landes, 2021]. Dans la continuité de ce résumé, et comme l’annonçait la conclusion de ce dernier, cette suite se concentre sur la question de la qualité des livrables 3D détaillés dans [Landes, 2020]. Numéro de notice : A2022-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101066
in XYZ > n° 171 (juin 2022) . - pp 14 - 24[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2022021 RAB Revue Centre de documentation En réserve L003 Disponible Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)
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[article]
Titre : Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area Type de document : Article/Communication Auteurs : Siming Yin, Auteur ; Xian Guo, Auteur ; Jie Jiang, Auteur Année de publication : 2022 Article en page(s) : n° 326 Note générale : résumé Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Streetview
[Termes IGN] paysage urbain
[Termes IGN] Pékin (Chine)
[Termes IGN] segmentation sémantique
[Termes IGN] site historiqueRésumé : (auteur) Accurate extraction of urban landscape features in the historic district of China is an essential task for the protection of the cultural and historical heritage. In recent years, deep learning (DL)-based methods have made substantial progress in landscape feature extraction. However, the lack of annotated data and the complex scenarios inside alleyways result in the limited performance of the available DL-based methods when extracting landscape features. To deal with this problem, we built a small yet comprehensive history-core street view (HCSV) dataset and propose a polarized attention-based landscape feature segmentation network (PALESNet) in this article. The polarized self-attention block is employed in PALESNet to discriminate each landscape feature in various situations, whereas the atrous spatial pyramid pooling (ASPP) block is utilized to capture the multi-scale features. As an auxiliary, a transfer learning module was introduced to supplement the knowledge of the network, to overcome the shortage of labeled data and improve its learning capability in the historic districts. Compared to other state-of-the-art methods, our network achieved the highest accuracy in the case study of Beijing Core Area, with an mIoU of 63.7% on the HCSV dataset; and thus could provide sufficient and accurate data for further protection and renewal in Chinese historic districts. Numéro de notice : A2022-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060326 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.3390/ijgi11060326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100760
in ISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 326[article]Feature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images / Hanwen Xu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)
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[article]
Titre : Feature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images Type de document : Article/Communication Auteurs : Hanwen Xu, Auteur ; Xinming Tang, Auteur ; Bo Ai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4411915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] segmentation multi-échelle
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Very-high-resolution (VHR) remote sensing images contain various multiscale objects, such as large-scale buildings and small-scale cars. However, these multiscale objects cannot be considered simultaneously in the widely used backbones with a large downsampling factor (e.g., VGG-like and ResNet-like), resulting in the appearance of various context aggregation approaches, such as fusing low-level features and attention-based modules. To alleviate this problem caused by backbones with a large downsampling factor, we propose a feature-selection high-resolution network (FSHRNet) based on an observation: if the features maintain high resolution throughout the network, a high precision segmentation result can be obtained by only using a 1× 1 convolution layer with no need for complex context aggregation modules. Specifically, the backbone of FSHRNet is a multibranch structure similar to HRNet where the high-resolution branch is the principal line. Then, a lightweight dynamic weight module, named the feature-selection convolution (FSConv) layer, is presented to fuse multiresolution features, allowing adaptively feature selection based on the characteristic of objects. Finally, a specially designed 1× 1 convolution layer derived from hypersphere embedding is used to produce the segmentation result. Experiments with other widely used methods show that the proposed FSHRNet obtains competitive performance on the ISPRS Vaihingen dataset, the ISPRS Potsdam dataset, and the iSAID dataset. Numéro de notice : A2022-559 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3183144 En ligne : https://doi.org/10.1109/TGRS.2022.3183144 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101184
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 4411915[article]Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)
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[article]
Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] bâtiment
[Termes IGN] Chine
[Termes IGN] détection de changement
[Termes IGN] espace vert
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699
in Remote sensing of environment > vol 274 (June 2022) . - n° 112993[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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[article]
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]Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)
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PermalinkRecent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)
PermalinkThe interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)
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PermalinkTowards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)
PermalinkTrue orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)
PermalinkAn informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)
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