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Titre : XXIV ISPRS Congress, Commission 2 Type de document : Actes de congrès Auteurs : Nicolas Paparoditis , Éditeur scientifique ; Clément Mallet
, Éditeur scientifique ; Florent Lafarge, Éditeur scientifique ; Fabio Remondino, Éditeur scientifique ; Isabella Toschi, Éditeur scientifique ; Takashi Fuse, Éditeur scientifique
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2020 Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 2 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] lasergrammétrie
[Termes IGN] photogrammétrie numériqueNuméro de notice : 17626 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans Date de publication en ligne : 06/08/2020 En ligne : https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/in [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97137 Voir aussiApplication of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems / Charlotte L. Priddy in Photogrammetric record, vol 34 n° 168 (December 2019)
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Titre : Application of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems Type de document : Article/Communication Auteurs : Charlotte L. Priddy, Auteur ; Jamie A. Pringle, Auteur ; Stuart M. Clarke, Auteur ; Ross P. Pettigrew, Auteur Année de publication : 2019 Article en page(s) : pp 428 - 444 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Agisoft Photoscan
[Termes IGN] Etats-Unis
[Termes IGN] image captée par drone
[Termes IGN] modèle de simulation
[Termes IGN] modèle géologique
[Termes IGN] modélisation 3D
[Termes IGN] photogrammétrie numérique
[Termes IGN] réalité virtuelle
[Termes IGN] réseau fluvial
[Termes IGN] sédiment
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) Outcrop studies are often used as analogues to subsurface sedimentaryreservoirs, with photogrammetry representing a useful technique to obtain quanti-tative geometrical data of sedimentary architectures. Digital photogrammetrictechniques were used to studyfluvial sediments of the Lower Jurassic KayentaFormation of the western USA. Model-extracted statistics for channel andsheetflood elements, relevant to reservoir modelling, were compared with 1D and2D datasets from the same outcrops. Results suggest that the 1D/2D datasignificantly underestimated element dimensions and ranges in ephemeralfluvialsystems. Consequently, this study demonstrates the value of photogrammetrictechniques for obtaining statistically relevant and more accurate reservoirmodelling input data from outcrops. Numéro de notice : A2019-577 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12299 Date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1111/phor.12299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94461
in Photogrammetric record > vol 34 n° 168 (December 2019) . - pp 428 - 444[article]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)
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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)
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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 Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)
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Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction automatique
[Termes IGN] gestion des itinéraires
[Termes IGN] jeu de données localisées
[Termes IGN] point de repère
[Termes IGN] précision de la classification
[Termes IGN] représentation mentale spatiale
[Termes IGN] saillance
[Termes IGN] Shenzhen
[Termes IGN] villeRésumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559
in International journal of geographical information science IJGIS > vol 33 n° 12 (December 2019) . - pp 2406 - 2423[article]Innovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)
PermalinkInside 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)
PermalinkA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)
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PermalinkA low‐cost open‐source workflow to generate georeferenced 3D SfM photogrammetric models of rocky outcrops / Laurent Froideval in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkMatching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
PermalinkUn modèle de transcription pour identifier et analyser les objets de référence et les relations spatiales utilisées pour se localiser en montagne / Mattia Bunel in Cartes & Géomatique, n° 241-242 (décembre 2019)
PermalinkPermalinkNumérisation, restitution et visualisation en 3D de sites patrimoniaux / Jonathan Chemla in XYZ, n° 161 (décembre 2019)
PermalinkOn the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
PermalinkPotentiel des sources de données collaboratives pour l'intégration de points de repère et des itinéraires pour le sauvetage en zone de montagne / Marie-Dominique Van Damme in Cartes & Géomatique, n° 241-242 (décembre 2019)
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