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Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces / Ali Karami in Photogrammetric record, vol 37 n° 177 (March 2022)
[article]
Titre : Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces Type de document : Article/Communication Auteurs : Ali Karami, Auteur ; Fabio Menna, Auteur ; Fabio Remondino, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 111 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] axe de prise de vue
[Termes IGN] étalonnage
[Termes IGN] figure géométrique
[Termes IGN] point d'appui
[Termes IGN] points homologues
[Termes IGN] rayonnement lumineux
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de pointsRésumé : (auteur) Three-dimensional (3D) measurement of non-collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image-matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre-processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image-matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting. Numéro de notice : A2022-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12400 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100218
in Photogrammetric record > vol 37 n° 177 (March 2022) . - pp 111 - 138[article]LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)
[article]
Titre : LiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland Type de document : Article/Communication Auteurs : Krystian Pyka, Auteur ; Radoslaw Piskorski, Auteur ; Aleksandra Jasińska, Auteur Année de publication : 2022 Article en page(s) : pp 476 - 495 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse visuelle
[Termes IGN] canyon urbain
[Termes IGN] Cracovie (Pologne)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] paysage urbain
[Termes IGN] piéton
[Termes IGN] point de repère
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] visibilité (optique)
[Termes IGN] visionRésumé : (auteur) We propose a method for analysing landmark visibility from a pedestrian’s perspective. A case study is performed in Kraków, a city with many architectural monuments, where airborne LiDAR is used to model both buildings and urban greenery. The proposed method involves preliminary and detailed stages. The preliminary stage entails an inverse analysis (I–Vis) that departs from the typical visibility analysis to enable the use of landmarks as observers instead of targets. I–Vis results in paths with high landmark visibility. The detailed stage involves the use of a virtual panorama (V-Pan) to determine the visual exposure of the landmarks. Landmarks considered visible by I–Vis are generally consistent with landmarks identified by V-Pan. Discrepancies occur when trees appear in the near field-of-view. In addition, the accuracy of the skyline length and visible landmark surface area is evaluated against ground observations. The obtained results show that V-Pan can evaluate landmark visibility with an accuracy of approximately 75%. The key contributions of the work to visibility analysis of urban landmarks are in the inverse viewshed strategy and evaluation of the visual exposure parameters on LiDAR virtual panoramas. Numéro de notice : A2022-206 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2015600 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2015600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100021
in International journal of geographical information science IJGIS > vol 36 n° 3 (March 2022) . - pp 476 - 495[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2022031 SL Revue Centre de documentation Revues en salle Disponible Monitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)
[article]
Titre : Monitoring coastal vulnerability by using DEMs based on UAV spatial data Type de document : Article/Communication Auteurs : Antonio Minervino Amodio, Auteur ; Gianluigi Di Paola, Auteur ; Carmen Maria Rosskopf, Auteur Année de publication : 2022 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Adriatique, mer
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géoréférencement
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] littoral méditerranéen
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotographie
[Termes IGN] point d'appui
[Termes IGN] structure-from-motion
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côte
[Termes IGN] vulnérabilitéRésumé : (auteur) The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application. Numéro de notice : A2022-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11030155 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.3390/ijgi11030155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99895
in ISPRS International journal of geo-information > vol 11 n° 3 (March 2022) . - n° 155[article]Neural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)
[article]
Titre : Neural map style transfer exploration with GANs Type de document : Article/Communication Auteurs : Sidonie Christophe , Auteur ; Samuel Mermet , Auteur ; Morgan Laurent, Auteur ; Guillaume Touya , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 18 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] grille d'échantillonnage
[Termes IGN] orthoimage
[Termes IGN] représentation cartographique
[Termes IGN] réseau antagoniste génératif
[Termes IGN] style cartographique
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Neural Style Transfer is a Computer Vision topic intending to transfer the visual appearance or the style of images to other images. Developments in deep learning nicely generate stylized images from texture-based examples or transfer the style of a photograph to another one. In map design, the style is a multi-dimensional complex problem related to recognizable visual salient features and topological arrangements, supporting the description of geographic spaces at a specific scale. The map style transfer is still at stake to generate a diversity of possible new styles to render geographical features. Generative adversarial Networks (GANs) techniques, well supporting image-to-image translation tasks, offer new perspectives for map style transfer. We propose to use accessible GAN architectures, in order to experiment and assess neural map style transfer to ortho-images, while using different map designs of various geographic spaces, from simple-styled (Plan maps) to complex-styled (old Cassini, Etat-Major, or Scan50 B&W). This transfer task and our global protocol are presented, including the sampling grid, the training and test of Pix2Pix and CycleGAN models, such as the perceptual assessment of the generated outputs. Promising results are discussed, opening research issues for neural map style transfer exploration with GANs. Numéro de notice : A2022-172 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2022.2031554 Date de publication en ligne : 13/02/2022 En ligne : https://doi.org/10.1080/23729333.2022.2031554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99807
in International journal of cartography > vol 8 n° 1 (March 2022) . - pp 18 - 36[article]Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
[article]
Titre : Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach Type de document : Article/Communication Auteurs : Linyuan Li, Auteur ; Xihan Mu, Auteur ; Francesco Chianucci, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] carte forestière
[Termes IGN] Chine
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couvert forestier
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] faisceau laser
[Termes IGN] forêt boréale
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] sous-étage
[Termes IGN] structure-from-motionRésumé : (auteur) Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision. Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation). Numéro de notice : A2022-192 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102686 Date de publication en ligne : 05/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99951
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102686[article]Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkUsing vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkAutomatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkPermalink3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)PermalinkPermalinkPermalink