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A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)
[article]
Titre : A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching Type de document : Article/Communication Auteurs : Chuang Qian, Auteur ; Hongjuan Zhang, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse comparative
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation de position
[Termes IGN] GPS assisté pour la navigation (technologies)
[Termes IGN] logique floue
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Despite the high-precision performance of GNSS real-time kinematic (RTK) in many cases, large noises in pseudo-range measurements or harsh signal environments still impact float ambiguity estimation in kinematic localization, which leads to ambiguity-fixed failure and worse positioning results. To improve RTK ambiguity resolution (AR) performance further, multi-sensor fusion technique is a feasible option. Light detection and ranging (LiDAR)-based localization is a good complementary method to GNSS. Tight integration of GNSS RTK and LiDAR adds new information to satellite measurements, thus improving float ambiguity estimation and then improving integer AR. In this work, a LiDAR aiding single-frequency single-epoch GPS + BDS RTK was proposed and investigated by theoretical analysis and performance assessment. Considering LiDAR-based localization failure because of ambiguous and repetitive landmarks, a fuzzy one-to-many feature-matching method was proposed to find a series of sequences including all possible relative positions to landmarks. Then, the standard RTK method was tightly combined with the possible positions from each sequence to find the most accurate position estimation. Experimental results proved the superiority of our method over the standard RTK method in all aspects of success rate, fixed rate and positioning accuracy. In specific, our method achieved centimeter-level position accuracy with 100% fixed rate in the urban environment, while the standard GPS + BDS RTK obtained decimeter-level accuracy with 26.84% fixed rate. In the high occlusion environment, our method had centimeter-level accuracy with a fixed rate of 96.31%, comparing a meter-level accuracy and a fixed rate of 7.65% of standard GPS + BDS RTK method. Numéro de notice : A2020-649 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01426-z Date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1007/s00190-020-01426-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96082
in Journal of geodesy > vol 94 n° 10 (October 2020) . - 18 p.[article]See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
[article]
Titre : See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning Type de document : Article/Communication Auteurs : Zhouxin Xi, Auteur ; Christopher Hopkinson, Auteur ; Stewart B. Rood, Auteur ; Derek R. Peddle, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espèce végétale
[Termes IGN] gestion forestière
[Termes IGN] semis de points
[Termes IGN] variation saisonnièreRésumé : (auteur) Determining tree species composition in natural forests is essential for effective forest management. Species classification at the individual tree level requires fine-scale traits which can be derived through terrestrial laser scanning (TLS) point clouds. A generalizable species classification framework also needs to decouple seasonal foliage variation from deciduous species, for which wood filtering is applicable. Different machine learning and deep learning models are feasible for wood filtering and species classification. We investigated 13 machine learning and deep learning classifiers for 9 species, and 15 classifiers for filtering wood points from TLS plot scans. Each classifier was evaluated using the criteria of mean Intersection over Union accuracy (mIoU), training stability and time cost. On average, deep learning classifiers outperformed machine learning classifiers by 10% and 5% in terms of wood and species classification mIoU, respectively. PointNet++ provided the best species classifier, with the highest mIoU (0.906), stability, and moderate time cost. Among wood classifiers, UNet achieved the top mIoU (0.839) while ResNet-50 was recommended for rapid trial and error testing. Across the classifications, the factors of input resolution, attributes and features were also analyzed. Hot zones of species classification with PointNet++ were visualized to indicate how AI interpret species traits. Numéro de notice : A2020-533 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.001 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95718
in ISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 1 - 16[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity / Sophie Davison in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)
[article]
Titre : The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity Type de document : Article/Communication Auteurs : Sophie Davison, Auteur ; Daniel N.M. Donoghue, Auteur ; Nikolaos Galiatsatos, Auteur Année de publication : 2020 Article en page(s) : n° 102160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] indicateur de biodiversité
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] peuplement forestier
[Termes IGN] semis de points
[Termes IGN] structure de la végétationRésumé : (auteur) Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence. Numéro de notice : A2020-749 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102160 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102160 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96399
in International journal of applied Earth observation and geoinformation > vol 92 (October 2020) . - n° 102160[article]Towards an optimization of sample plot size and scanner position layout for terrestrial laser scanning in multi-scan mode / Tim Ritter in Forests, vol 11 n° 10 (October 2020)
[article]
Titre : Towards an optimization of sample plot size and scanner position layout for terrestrial laser scanning in multi-scan mode Type de document : Article/Communication Auteurs : Tim Ritter, Auteur ; Christoph Gollob, Auteur ; Arne Northdurft, Auteur Année de publication : 2020 Article en page(s) : n° 1099 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Autriche
[Termes IGN] balayage laser
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] placette d'échantillonnage
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) A novel approach is presented to model the tree detection probability of terrestrial laser scanning (TLS) in forest inventory applications using a multi-scan mode. The traditional distance sampling framework is further extended to account for multiple scan positions at a single sample plot and to allow for an imperfect detection probability at distance r = 0. The novel methodology is tested with real world data, as well as in simulations. It is shown that the underlying detection model can be parameterized using only data from single scans. Hereby, it is possible to predict the detection probability also for different sample plot sizes and scanner position layouts in a multi-scan setting. Simulations showed that a minor discretization bias can occur if the sample size is small. The methodology enables a generalized optimization of the scanning layout in a multi-scan setting with respect to the detection probability and the sample plot area. This will increase the efficiency of multi-scan TLS-based forest inventories in the future. Numéro de notice : A2020-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11101099 Date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.3390/f11101099 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96438
in Forests > vol 11 n° 10 (October 2020) . - n° 1099[article]Tree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
[article]
Titre : Tree species classification using structural features derived from terrestrial laser scanning Type de document : Article/Communication Auteurs : Louise Terryn, Auteur ; Kim Calders, Auteur ; Mathias I. Disney, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 170 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] arbre (flore)
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] composition d'un peuplement forestier
[Termes IGN] couvert forestier
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espèce végétale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] ombre
[Termes IGN] régression logistique
[Termes IGN] semis de pointsRésumé : (auteur) Fast and automated collection of forest data, such as species composition information, is required to support climate mitigation actions. Recently, there have been significant advances in the use of terrestrial laser scanning (TLS) instruments, which facilitate the capture of detailed forest structure. However, for tree species recognition the structural information from TLS has mainly been used to complement spectral information. TLS-only classification studies have been limited in size and diversity of plot forest types. In this paper, we investigate the potential of TLS for tree species classification. We used quantitative structure models to determine 17 structural tree features. These features were computed for 758 trees of five tree species, including two understory species, of a 1.4 hectare mixed deciduous forest plot. Three classification methods were compared: k-nearest neighbours, multinomial logistic regression and support vector machine. We assessed the potential underlying causes for structural differences with principal component analysis. We obtained classification success rates of approximately 80%, however, with producer accuracies for three of the five species ranging from 0 to 60%. Low producer accuracies were the result of a high intra- and low inter-species variability. These effects were, respectively, caused by a high size-dependency of the structural features and a convergence of structural traits across species as a result of the individual tree position in the forest canopy and shade tolerance. Nevertheless, the producer accuracies could be improved through sensitivity vs. specificity trade-offs, with over 50% for all species being obtainable. The high intra -and low inter-species variability complicate the classification. Furthermore, the classification performance and best classification method greatly depend on its targeted application. In conclusion, this study proves the added value of TLS for tree species classification but also shows that TLS opens up potential for testing and further development of ecological theory. Numéro de notice : A2020-636 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.009 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96059
in ISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 170 - 181[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Weighted spherical sampling of point clouds for forested scenes / Alex Fafard in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)PermalinkApplication of UAV photogrammetry with LiDAR data to facilitate the estimation of tree locations and DBH values for high-value timber species in Northern Japanese mixed-wood forests / Kyaw Thu Moe in Remote sensing, vol 12 n° 17 (September-1 2020)PermalinkRelevé 3D et classification de nuages de points de patrimoine bâti / Arnadi Murtiyoso in XYZ, n° 164 (septembre 2020)PermalinkShallow water bathymetry derived from green wavelength terrestrial laser scanner / Theodore Panagou in Marine geodesy, Vol 43 n° 5 (September 2020)PermalinkPlanar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkProvably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkAssessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa / Priyadarshi Upadhyay in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkRoles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkUnsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkHétérogénéité des distances : quel impact sur la qualité des relevés lidar aériens et terrestres ? / Laurent Polidori in XYZ, n° 163 (juin 2020)PermalinkMapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors / Svetlana Saarela in Forest ecosystems, vol 7 (2020)PermalinkMapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data / Johannes Schumacher in Forest ecosystems, vol 7 (2020)PermalinkModélisation d'une maquette sur la base de données LiDAR et intégration d'un projet 3D / Julien Brunner in Géomatique suisse, vol 118 n° 6 (juin 2020)PermalinkUnder-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkAssessment of the accuracy of DTM river bed model using classical surveying measurement and LiDAR: a case study in Poland / Pawel Kotlarz in Survey review, vol 52 n° 372 (May 2020)PermalinkFiltering of airborne LiDAR bathymetry based on bidirectional cloth simulation / Anxiu Yang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkImproved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkMapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkMethod for extraction of airborne LiDAR point cloud buildings based on segmentation / Maohua Liu in Plos one, vol 15 n° 5 (May 2020)PermalinkOutlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)PermalinkLa télédétection aéroportée pour la gestion des territoires forestiers de montagne / Jean-Matthieu Monnet in Sciences, eaux & territoires, n° 33 (avril 2020)PermalinkDirectionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])PermalinkMultitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data / Tomás Fernández in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkUsing multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)PermalinkHow far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)Permalink3D laser scanning of the natural caves: Example of Škocjanske jame / Richard Walters in Geodetski vestnik, Vol 64 n° 1 (March - May 2020)PermalinkAn improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)PermalinkClassification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkA discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkGeneration of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)PermalinkHierarchical classification of pole‐like objects in mobile laser scanning point clouds / Rufei Liu in Photogrammetric record, vol 35 n° 169 (March 2020)PermalinkIntegration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkLearning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkObject-based incremental registration of terrestrial point clouds in an urban environment / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkUnsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)PermalinkAutomated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkA LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams / Benjamin Swan in Transactions in GIS, Vol 24 n° 1 (February 2020)PermalinkThree-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkTree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkApplication of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)PermalinkPermalinkCartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)PermalinkContribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)PermalinkPermalinkDétection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie / Etienne Barçon (2020)PermalinkPermalinkEstimation 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)PermalinkFusion of 3D point clouds and hyperspectral data for the extraction of geometric and radiometric features of trees / Eduardo Alejandro Tusa Jumbo (2020)PermalinkDe l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond / Lionel Matteo (2020)PermalinkMise en place d'une méthode de détermination de la hauteur d'eau des océans à partir d'un capteur LiDAR aéroporté dans le cadre de la calibration/validation de l'altimètre SWOT / Romain Serthelon (2020)PermalinkMoving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)PermalinkPermalinkOn the adjustment, calibration and orientation of drone photogrammetry and laser-scanning / Emmanuel Clédat (2020)PermalinkPoint cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)PermalinkPratique des relevés en zones urbaines denses intégrant les nouvelles technologies / Théo Laporte (2020)PermalinkPredicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model / Paulina T. Marczak in Remote sensing, vol 12 n° 1 (January 2020)PermalinkPermalinkPermalinkRelevés par Lidar mobile de cours d’eau et intégration des profils aux relevés bathymétriques réalisés par sondeur mono-faisceau / Guillaume Didier (2020)PermalinkPermalinkPermalinkPermalinkPermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)PermalinkPermalinkPermalinkThree-dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning / Richard David Williams in Earth surface processes and landforms, vol 45 n° 1 (January 2020)PermalinkA versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions / Thanh Huy Nguyen (2020)PermalinkDeep 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)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)PermalinkNouvelle donne aérienne / Marielle Mayo in Géomètre, n° 2175 (décembre 2019)PermalinkNumérisation, restitution et visualisation en 3D de sites patrimoniaux / Jonathan Chemla in XYZ, n° 161 (décembre 2019)PermalinkSemiautomatically 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)PermalinkPostprocessing 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)PermalinksUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October-1 2019)PermalinkTransferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)PermalinkBurn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkComparison 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)PermalinkDelineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkIntegration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)PermalinkPpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)PermalinkReduction of measurement data before Digital Terrain Model generation vs. DTM generalisation / Wioleta Błaszczak-Bąk in Survey review, vol 51 n° 368 (September 2019)PermalinkQuantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale / Elena Barbierato in European journal of remote sensing, vol 52 n° 4 (2019)PermalinkAutomatic extraction of accurate 3D tie points for trajectory adjustment of mobile laser scanners using aerial imagery / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkExplanation 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)PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkModelling 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)PermalinkPavement marking retroreflectivity estimation and evaluation using mobile Lidar data / Erzhuo Che in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 8 (August 2019)PermalinkSemantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkComparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)PermalinkEmpirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkInnovations in ground and airborne technologies as reference and for training and validation: Terrestrial Laser Scanning (TLS) / Mathias I. Disney in Surveys in Geophysics, vol 40 n° 4 (July 2019)Permalink