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Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 129 - 139 Titre : Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Jin Chen, Auteur ; Weiguo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 129 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données TLS (télémétrie) [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] littoral [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] rayonnement proche infrarouge [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] teneur en vapeur d'eau [Termes descripteurs IGN] vase Résumé : (Auteur) Estimations of the soil surface water contents and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats. However, existing techniques have limitations. Long-range terrestrial laser scanners (TLSs) can record the co-located intensity value which refers to a measure of the backscattered laser from each scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by water. Thus, the intensity values can be used as proxies for water contents. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects to quantitatively estimate the soil surface water contents of intertidal mudflats. A case study for a mudflat in Chongming Island, Shanghai, China, is conducted. Results indicate that compared with traditional techniques, the corrected intensity data of long-range TLSs are extremely effective data sources for a quick, accurate, and detailed estimation of water contents for large-area mudflats. The estimation root mean square error is approximately 3%. Furthermore, the 3D distributions of the water contents can be accurately mapped by combining the point cloud of the mudflats to potentially analyze the intrinsic association among water contents and topography, vegetation coverage, and habitation of creatures in mudflats. Numéro de notice : A2020-013 Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.isprsjprs.2019.11.003 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94402 [article]

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Validation and verification procedures for defining legal 3D boundaries using terrestrial laser scanners / Sam Rondeel in Survey review, Vol 52 n°370 (Janvier 2020)  

Public [article]  inSurvey review > Vol 52 n°370 (Janvier 2020) . - pp 69 - 75 Titre : Validation and verification procedures for defining legal 3D boundaries using terrestrial laser scanners Type de document : Article/Communication Auteurs : Sam Rondeel, Auteur ; Michael Barry, Auteur ; Derek D. Lichti, Auteur Année de publication : 2020 Article en page(s) : pp 69 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] délimitation de frontière [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données TLS (télémétrie) [Termes descripteurs IGN] étalonnage de capteur (imagerie) [Termes descripteurs IGN] géomètre [Termes descripteurs IGN] instrument de mesure [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] tachéomètre électronique [Termes descripteurs IGN] topographie Résumé : (auteur) A gap exists in law and practice with respect to calibrating terrestrial laser scanners (TLS) for legal boundary surveying. Laws, calibration infrastructure and calibration procedures have long existed for electronic distance measurement units used for boundary surveys, but laser scanners present new challenges. The aim of this paper is to present a practical, inexpensive procedure for validating laser scanner calibration and for verifying surface information extracted from terrestrial laser scanner data. The methodology verifies the TLS calibration parameters and extracted surfaces using total station measurements. The results indicate that total stations and TLS are complimentary tools and should both be used where complicated 3D boundaries exist. Numéro de notice : A2020-026 Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1518800 date de publication en ligne : 08/09/2018 En ligne : https://doi.org/10.1080/00396265.2018.1518800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94474 [article]

Application of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems / Charlotte L. Priddy in Photogrammetric record, vol 34 n° 168 (December 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 428 - 444 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 descripteurs IGN] Agisoft Photoscan [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle géologique [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] réalité virtuelle [Termes descripteurs IGN] réseau fluvial [Termes descripteurs IGN] sédiment [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure-from-motion Résumé : (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 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 [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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 219 - 230 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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] houppier [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points Résumé : (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 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 [article]

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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)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 346 - 364 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 descripteurs IGN] Antarctique [Termes descripteurs IGN] banquise [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] image radar [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pas d'échantillonnage au sol [Termes descripteurs IGN] réalité augmentée [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] travail coopératif [Termes descripteurs IGN] VRML Résumé : (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 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 [article]

Comparative study of photogrammetry software in industrial field / Saif Aati in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)

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Semiautomatically register MMS LiDAR points and panoramic image sequence using road lamp and lane / Ningning Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)  

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Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Postprocessing synchronization of a laser scanning system aboard a UAV / Marcela do Valle Machado in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

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Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Enhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

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The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests / Christopher Mulverhill in Annals of Forest Science [en ligne], Vol 76 n° 3 (September 2019)  

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Explanation for the seam line discontinuity in terrestrial laser scanner point clouds / Derek D. Lichti in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Modelling of buildings from aerial LiDAR point clouds using TINs and label maps / Minglei Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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