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Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)
[article]
Titre : Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy Type de document : Article/Communication Auteurs : Zoë E. Wakeford, Auteur ; Magda Chmielewska, Auteur ; Malcolm J. Hole, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 445 - 466 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données GNSS
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] modélisation 3D
[Termes IGN] photogrammétrie numérique
[Termes IGN] point d'appui
[Termes IGN] risque naturel
[Termes IGN] spectre électromagnétique
[Termes IGN] Stromboli (volcan)
[Termes IGN] surveillance géologiqueRésumé : (auteur) Volcanoes are a potential hazard to over 750 million people worldwide. Accessing them to install monitoring equipment can be logistically challenging and dangerous. Traditional monitoring equipment is expensive and not available to many local communities. A new, low‐cost method is proposed to address these challenges using a unique 3D thermal photogrammetric modelling workflow. The data acquisition and processing part of this workflow has been tested on Stromboli, a volcano in the Aeolian Islands of Italy. Unmanned aerial vehicles (UAVs) were deployed at the volcano to acquire both visible and thermal infrared imagery. Both datasets were then digitally processed to produce 3D virtual outcrop models. Finally, the two datasets and models were integrated to produce the first 3D thermal photogrammetric model of an active volcano. The result is an easy‐to‐use workflow applicable to any volcano. This low‐cost monitoring system could be deployed in developing countries and remote areas otherwise hindered by limited budgets and poor access. Numéro de notice : A2019-578 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12301 Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1111/phor.12301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94464
in Photogrammetric record > vol 34 n° 168 (December 2019) . - pp 445 - 466[article]Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
[article]
Titre : Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Nathan B. Jacobs, Auteur ; Marco A. Contreras, Auteur ; Chase H. Clark, Auteur Année de publication : 2019 Article en page(s) : pp 219 - 230 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] arbre caducifolié
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] houppier
[Termes IGN] modèle numérique de surface
[Termes IGN] Pinophyta
[Termes IGN] semis de pointsRésumé : (auteur) The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees segmented from airborne LiDAR data. To enable processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM × 4) and a set of four 2D views (4 × 2D). A training dataset of tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples. Benchmarked against multiple traditional shallow learning methods using manually designed features, the CNNs improved accuracies up to 14%. The 4 × 2D representation yielded similar classification accuracies to the DSM × 4 representation (~82% coniferous and ~90% deciduous) while converging faster. Further experimentation showed that early/late fusion of the channels in the representations did not affect the accuracies in a significant way. The data augmentation that was used for the CNN training improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvement. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. In domains like remote sensing and biomedical imaging, where the data contain a large amount of information and are not friendly to human visual system, human-designed features may become suboptimal. As exemplified by this study, automatic, objective derivation of optimal features via deep learning can improve prediction tasks in such domains. Numéro de notice : A2019-547 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.011 Date de publication en ligne : 03/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94192
in ISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 219 - 230[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019123 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019122 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Innovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)
[article]
Titre : Innovative techniques of photogrammetry for 3D modeling Type de document : Article/Communication Auteurs : Vicenzo Barrile, Auteur ; Alice Pozzoli, Auteur ; Giuliana Bilotta, Auteur ; Antonino Fotia, Auteur Année de publication : 2019 Article en page(s) : pp 353–369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie analytique
[Termes IGN] Italie
[Termes IGN] modèle non linéaire
[Termes IGN] modélisation 3D
[Termes IGN] orientation absolue
[Termes IGN] orientation automatique
[Termes IGN] orientation relative
[Termes IGN] Raspberry Pi
[Termes IGN] reconstruction d'image
[Termes IGN] structure-from-motion
[Termes IGN] vision par ordinateurRésumé : (auteur) This note presents the experimental results deriving from the application of two innovative photogrammetric techniques (with particular reference to non-conventional photogrammetric applications) for the production of time-space 3D models of the marine surface. Moreover, the first method (automatic three images processing (ATIP)) proposes some easy procedures to solve typical non-linear problems of analytical photogrammetry. In particular, once validated the technique of orientation of two images (two-step procedure based on two phases: relative orientation and absolute orientation, both characterized by non-linear functions), we propose a procedure for the automatic orientation of three images (the introduction of a third image allows avoiding human decision to find the final solution). The second method (Computer Vision Raspberry Pi—CVR) refers to the use of the “prompt” technique of computer vision (structure from motion) using five appropriately synchronized cameras to acquire simultaneously the various frames, thanks to the use of an acquisition system based on the use of Raspberry Pi. The experimentation was conducted both in the laboratory (on a model that allows to study a typical phenomenon of the Alpine Valtellina region, in the North of Italy) that directly at sea (on a portion of marine surface located in Reggio Calabria near the seafront). The results obtained show a substantial comparability of the results both between the two methods and with the actual data measured at sea with dedicated instrumentation. Numéro de notice : A2019-533 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00264-9 Date de publication en ligne : 22/05/2019 En ligne : https://doi.org/10.1007/s12518-019-00264-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94126
in Applied geomatics > Vol 11 n° 4 (December 2019) . - pp 353–369[article]Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)
[article]
Titre : Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica Type de document : Article/Communication Auteurs : Alexandra L. Boghosian, Auteur ; Martin J. Pratt, Auteur ; Maya A. Becker, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 346 - 364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Antarctique
[Termes IGN] banquise
[Termes IGN] couplage GNSS-INS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] glace de mer
[Termes IGN] image radar
[Termes IGN] Matlab
[Termes IGN] modèle numérique de surface
[Termes IGN] pas d'échantillonnage au sol
[Termes IGN] réalité augmentée
[Termes IGN] semis de points
[Termes IGN] travail coopératif
[Termes IGN] VRMLRésumé : (auteur) From 2015 to 2017, the ROSETTA‐Ice project comprehensively mapped Antarctica's Ross Ice Shelf using IcePod, a newly developed aerogeophysical platform. The campaign imaged the ice‐shelf surface with lidar and its internal structure with ice‐penetrating radar. The ROSETTA‐Ice data was combined with pre‐existing ice surface and bed topography digital elevation models to create the first augmented reality (AR) visualisation of the Antarctic Ice Sheet, using the Microsoft HoloLens. The ROSETTA‐Ice datasets support cross‐disciplinary science that aims to understand 4D processes, namely the change of 3D ice‐shelf structures over time. The work presented here uses AR to visualise this dataset in 3D and highlights how AR can be simultaneously a useful research tool for interdisciplinary geoscience as well as an effective device for science communication education. Numéro de notice : A2019-575 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12298 Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1111/phor.12298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94455
in Photogrammetric record > vol 34 n° 168 (December 2019) . - pp 346 - 364[article]A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)
[article]
Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bâti-3D
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'erreur
[Termes IGN] données localisées
[Termes IGN] France (administrative)
[Termes IGN] image à très haute résolution
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle d'erreur
[Termes IGN] modèle numérique de surface
[Termes IGN] qualité des données
[Termes IGN] taxinomieRésumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Numéro de notice : A2019-569 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 Date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878[article]Réservation
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