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Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping / Luc Baudoux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp ? Titre : Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Année de publication : 2023 Projets : AI4GEO / Article en page(s) : pp ? Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] apprentissage profond [Termes IGN] carte d'occupation du sol [Termes IGN] carte d'utilisation du sol [Termes IGN] carte thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] harmonisation des données [Termes IGN] nomenclature [Termes IGN] pouvoir de résolution géométrique Résumé : (auteur) Land-use/land-cover (LULC) maps describe the Earth’s surface with discrete classes at a specific spatial resolution. The chosen classes and resolution highly depend on peculiar uses, making it mandatory to develop methods to adapt these characteristics for a large range of applications. Recently, a convolutional neural network (CNN)-based method was introduced to take into account both spatial and geographical context to translate a LULC map into another one. However, this model only works for two maps: one source and one target. Inspired by natural language translation using multiple-language models, this article explores how to translate one LULC map into several targets with distinct nomenclatures and spatial resolutions. We first propose a new data set based on six open access LULC maps to train our CNN-based encoder-decoder framework. We then apply such a framework to convert each of these six maps into each of the others using our Multi-Landcover Translation network (MLCT-Net). Extensive experiments are conducted at a country scale (namely France). The results reveal that our MLCT-Net outperforms its semantic counterparts and gives on par results with mono-LULC models when evaluated on areas similar to those used for training. Furthermore, it outperforms the mono-LULC models when applied to totally new landscapes. Numéro de notice : A2023-075 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2120996 Date de publication en ligne : 10/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2120996 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101797 [article]

In-camera IMU angular data for orthophoto projection in underwater photogrammetry / Erica Nocerino in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 7 (January 2023) . - n° 100027 Titre : In-camera IMU angular data for orthophoto projection in underwater photogrammetry Type de document : Article/Communication Auteurs : Erica Nocerino, Auteur ; Fabio Menna, Auteur Année de publication : 2023 Article en page(s) : n° 100027 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] caméra numérique [Termes IGN] carte bathymétrique [Termes IGN] centrale inertielle [Termes IGN] compensation par faisceaux [Termes IGN] mesure géodésique [Termes IGN] orthophotographie [Termes IGN] photogrammétrie sous-marine [Termes IGN] positionnement par GNSS [Termes IGN] redressement différentiel [Termes IGN] roulis [Termes IGN] structure-from-motion [Termes IGN] tangage Résumé : (auteur) Among photogrammetric products, orthophotos are probably the most versatile and widely used in many fields of application. In the last years, coupled with the spread of semi-automated survey and processing approaches based on photogrammetry, orthophotos have become almost a standard for monitoring the underwater environment. If on land the definition of the reference coordinate system and projection plane for the orthophoto generation is trivial, underwater it may represent a challenge. In this paper, we address the issue of defining the vertical direction and resulting horizontal plane (levelling) for the differential ortho rectification. We propose a non-invasive, contactless method based on roll and pitch angular data provided by in-camera IMU sensors and embedded in the Exif metadata of JPEG and raw image files. We show how our approach can be seamlessly integrated into automatic SfM/MVS pipelines, provide the mathematical background, and showcase real-world applications results in an underwater monitoring project. The results illustrate the effectiveness of the proposed method and, for the first time, provide a metric evaluation of the definition of the vertical direction with low-cost sensors enclosed in digital cameras directly underwater. Numéro de notice : A2023-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Numéro de périodique DOI : 10.1016/j.ophoto.2022.100027 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102493 [article]

A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration / Tarek Hassan in Journal of applied geodesy, vol 17 n° 1 (January 2023)  

Public [article]  inJournal of applied geodesy > vol 17 n° 1 (January 2023) . - pp 65 - 77 Titre : A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration Type de document : Article/Communication Auteurs : Tarek Hassan, Auteur ; Tamer Fath-Allah, Auteur ; Mohamed Elhabiby, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 65 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] capteur à balayage [Termes IGN] centrale inertielle [Termes IGN] gyroscope [Termes IGN] lidar mobile [Termes IGN] odomètre [Termes IGN] panne [Termes IGN] positionnement par GNSS [Termes IGN] système de transport intelligent [Termes IGN] temps réel [Termes IGN] véhicule automobile [Termes IGN] zone urbaine Résumé : (auteur) Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively. Numéro de notice : A2023-110 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2022-0022 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.1515/jag-2022-0022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102469 [article]

Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms / Yadong Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 3000224 Titre : Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms Type de document : Article/Communication Auteurs : Yadong Li, Auteur ; Sébastien Mavromatis, Auteur ; Feng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3000224 Note générale : bibliographie 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] image isolée [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale [Termes IGN] reconstruction d'image [Termes IGN] réseau antagoniste génératif Résumé : (auteur) Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth’s surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines L1 pixel loss, L1 perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method. Numéro de notice : A2022-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3093043 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3093043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101789 [article]

Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 102999 Titre : Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest Type de document : Article/Communication Auteurs : Daniel Kükenbrink, Auteur ; Mauro Marty, Auteur ; Ruedi Bösch, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102999 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] caméra à bas coût [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lidar mobile [Termes IGN] lidar topographique [Termes IGN] photogrammétrie terrestre [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] Zurich (Suisse) Résumé : (auteur) National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction. The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%. Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated. Numéro de notice : A2022-787 Affiliation des auteurs : IGN (1940-2011) Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102999 Date de publication en ligne : 05/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101893 [article]

Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)  

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Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt / André Bertoncini in Remote sensing of environment, vol 278 (September 2022)  

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3D browsing of wide-angle fisheye images under view-dependent perspective correction / Mingyi Huang in Photogrammetric record, vol 37 n° 178 (June 2022)  

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La puissance spatiale chinoise s’affirme / Laurent Polidori in Géomètre, n° 2203 (juin 2022)

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Smartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)  

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PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

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Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)  

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Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)  

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Apport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse / Nesrine Farhani (2022)  

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Calibration radiométrique et géométrique d'une caméra fish-eye pour la mesure de l'hémisphère de luminance incidente / Manchun Lei (2022)

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