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Exploring tree growth allometry using two-date terrestrial laser scanning / Tuomas Yrttimaa in Forest ecology and management, vol 518 (15 August 2022)  

Public [article]  inForest ecology and management > vol 518 (15 August 2022) . - n° 120303 Titre : Exploring tree growth allometry using two-date terrestrial laser scanning Type de document : Article/Communication Auteurs : Tuomas Yrttimaa, Auteur ; Ville Luoma, Auteur ; Ninni Saarinen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] allométrie [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt boréale [Termes IGN] houppier [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] surface terrière [Termes IGN] volume en bois Résumé : (auteur) Tree growth is a physio-ecological phenomena of high interest among researchers across disciplines. Observing changes in tree characteristics has conventionally required either repeated measurements of the characteristics of living trees, retrospective measurements of destructively sampled trees, or modelling. The use of close-range sensing techniques such as terrestrial laser scanning (TLS) has enabled non-destructive approaches to reconstruct the three-dimensional (3D) structure of trees and tree communities in space and time. This study aims at improving the understanding of tree allometry in general and interactions between tree growth and its neighbourhood in particular by using two-date point clouds. We investigated how variation in the increments in basal area at the breast height (Δg1.3), basal area at height corresponding to 60% of tree height (Δg06h), and volume of the stem section below 50% of tree height (Δv05h) can be explained with TLS point cloud-based attributes characterizing the spatiotemporal structure of a tree crown and crown neighbourhood, entailing the competitive status of a tree. The analyses were based on 218 trees on 16 sample plots whose 3D characteristics were obtained at the beginning (2014, T1) and at the end of the monitoring period (2019, T2) from multi-scan TLS point clouds using automatic point cloud processing methods. The results of this study showed that, within certain tree communities, strong relationships (|r| > 0.8) were observed between increments in the stem dimensions and the attributes characterizing crown structure and competition. Most often, attributes characterizing the competitive status of a tree, and the crown structure at T1, were the most important attributes to explain variation in the increments of stem dimensions. Linear mixed-effect modelling showed that single attributes could explain up to 35–60% of the observed variation in Δg1.3, Δg06h and Δv05h, depending on the tree species. This tree-level evidence of the allometric relationship between stem growth and crown dynamics can further be used to justify landscape-level analyses based on airborne remote sensing technologies to monitor stem growth through the structure and development of crown structure. This study contributes to the existing knowledge by showing that laser-based close-range sensing is a feasible technology to provide 3D characterization of stem and crown structure, enabling one to quantify structural changes and the competitive status of trees for improved understanding of the underlying growth processes. Numéro de notice : A2022-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120303 Date de publication en ligne : 22/05/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100899 [article]

Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)  

Public [article]  inSurvey review > vol 54 n° 385 (July 2022) . - pp 363 - 374 Titre : Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners Type de document : Article/Communication Auteurs : Artu Ellmann, Auteur ; Kaia Kütimets, Auteur ; Sander Varbla, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 363 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arpentage [Termes IGN] cartographie et localisation simultanées [Termes IGN] données lidar [Termes IGN] Estonie [Termes IGN] géoréférencement [Termes IGN] industrie minière [Termes IGN] mine [Termes IGN] modélisation 3D [Termes IGN] schiste [Termes IGN] semis de points [Termes IGN] système de numérisation mobile [Termes IGN] télémètre laser terrestre Résumé : (auteur) Applicability of SLAM (simultaneous localization and mapping) technology for mine surveys and subsequent 3D modelling of post-extracted surfaces is assessed. The resulting surface geometry is validated via terrestrial laser scanner (TLS) acquired reference data. Typical discrepancies remained within 2 and 5 cm in horizontal and vertical directions, respectively. Discrepancies between TLS, SLAM-enabled handheld scanner and conventional surveying results are small and fully satisfy the contemporary accuracy requirements, yet evidence that the conventional mine survey results are affected by the subjectivity of the surveyors. The SLAM-enabled laser scanning hence appears to be the most suitable method for underground mining surveys. Numéro de notice : A2022-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1944545 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1944545 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101093 [article]

Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 185 - 200 Titre : Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks Type de document : Article/Communication Auteurs : Aurélien Brun, Auteur ; Davide Antonio Cucci, Auteur ; Jan Skaloud, Auteur Année de publication : 2022 Article en page(s) : pp 185 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] appariement de points [Termes IGN] centrale inertielle [Termes IGN] données lidar [Termes IGN] filtre de Kalman [Termes IGN] géoréférencement [Termes IGN] précision du positionnement [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points [Termes IGN] signal GNSS [Termes IGN] superposition Résumé : (auteur) With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point correspondences between overlapping point clouds and their joint integration (adjustment) together with raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a dynamic network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. The 3D correspondences are formulated as static conditions within the dynamic network and the registered point cloud is generated with significantly higher accuracy based on the corrected trajectory and possibly other parameters determined within the adjustment. We first describe the method for selecting correspondences and how they are inserted into the dynamic network via new observation model while providing an open-source implementation of the solver employed in this work. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors. In the conducted experiments, the method proposed to establish 3D correspondences is effective in determining point–to–point matches across a wide range of geometries such as trees, buildings and cars. Our results demonstrate that the method improves the point cloud registration accuracy (5 in nominal and 10 in emulated GNSS outage conditions within the studied cases), which is otherwise strongly affected by errors in the determined platform attitude or position, and possibly determine unknown boresight angles. The proposed methods remain effective even if only a fraction (0.1%) of the total number of established 3D correspondences are considered in the adjustment. Numéro de notice : A2022-413 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.027 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100764 [article]

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Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 63 - 77 Titre : Street-view imagery guided street furniture inventory from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yuzhou Zhou, Auteur ; Xu Han, Auteur ; Mingjun Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image Streetview [Termes IGN] instance [Termes IGN] inventaire [Termes IGN] jeu de données localisées [Termes IGN] masque [Termes IGN] mobilier urbain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] séparateur à vaste marge [Termes IGN] Shanghai (Chine) [Termes IGN] Wuhan (Chine) Résumé : (auteur) Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. Numéro de notice : A2022-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2022.04.023 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100711 [article]

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Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 188 (June 2022) . - pp 45 - 61 Titre : Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification Type de document : Article/Communication Auteurs : Yongqiang Mao, Auteur ; Kaiqiang chen, Auteur ; Wenhui Diao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 45 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] Perceptron multicouche [Termes IGN] représentation parcimonieuse [Termes IGN] réseau neuronal de graphes [Termes IGN] semis de points [Termes IGN] stratification de données [Termes IGN] voxel Résumé : (Auteur) The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging for the distinguishment of the areas with complex structures and extreme scale variations. In this article, for the objective of configuring multi-receptive field features, we propose a novel receptive field fusion-and-stratification network (RFFS-Net). With a novel dilated graph convolution (DGConv) and its extension annular dilated convolution (ADConv) as basic building blocks, the receptive field fusion process is implemented with the dilated and annular graph fusion (DAGFusion) module, which obtains multi-receptive field feature representation through capturing dilated and annular graphs with various receptive regions. The stratification of the receptive fields with point sets of different resolutions as the calculation bases is performed with Multi-level Decoders nested in RFFS-Net and driven by the multi-level receptive field aggregation loss (MRFALoss) to drive the network to learn in the direction of the supervision labels with different resolutions. With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net significantly outperforms the baseline (i.e. PointConv) approach by 5.3% on mF1 and 5.4% on mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU of 58.2%. The experiments show that our RFFS-Net achieves a new state-of-the-art classification performance on powerline, car, and fence classes. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art classification performance. The code is available at github.com/WingkeungM/RFFS-Net. Numéro de notice : A2022-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.019 Date de publication en ligne : 07/04/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100532 [article]

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Direct and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system / Eric Hyyppä in Science of remote sensing, vol 5 (June 2022)  

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Évaluation de la qualité de modèles 3D issus de nuages de points / Tania Landes in XYZ, n° 171 (juin 2022)

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Automatic training data generation in deep learning-aided semantic segmentation of heritage buildings / Arnadi Murtiyoso in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Cliff change detection using siamese KPCONV deep network on 3D point clouds / Iris de Gelis in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

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K-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Railway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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A voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results / N. Homainejad in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

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3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)  

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City3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)  

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