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Pavement marking retroreflectivity estimation and evaluation using mobile Lidar data / Erzhuo Che in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 8 (August 2019)
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Titre : Pavement marking retroreflectivity estimation and evaluation using mobile Lidar data Type de document : Article/Communication Auteurs : Erzhuo Che, Auteur ; Michael J. Olsen, Auteur ; Christopher E. Parrish, Auteur ; Jaehoon Jung, Auteur Année de publication : 2019 Article en page(s) : pp 573 - 583 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage radiométrique
[Termes IGN] réflectivité
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] signalisation routièreRésumé : (Auteur) Pavement markings are produced with retroreflective materials to enhance visibility for motorists, particularly at night. Retroreflectivity evaluation throughout an extensive highway network for maintenance and asset management purposes is a critical, yet challenging task for transportation agencies because visual evaluation can often be subjective and inconsistent, while field measurement can be time-consuming. Mobile Light Detection and Ranging (Lidar) datasets can potentially provide a safe, cost-effective, and reliable method of performing the required evaluation. This paper presents an empirical model for radiometric calibration of Lidar intensity information from the Leica Pegasus:Two system for pavement marking evaluation. The model was developed using dense handheld retroreflectometer measurements and mobile Lidar data collected in a variety of geometric configurations on a test site consisting of various markings with varying degrees of wear. The quantitative accuracy assessment of the proposed radiometric calibration model for estimating retroreflectivity was conducted to another independent dataset collected in different lanes and system configurations. Numéro de notice : A2019-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.8.573 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.14358/PERS.85.8.573 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93540
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 8 (August 2019) . - pp 573 - 583[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019081 SL Revue Centre de documentation Revues en salle Disponible Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
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Titre : Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Florent Lafarge, Auteur Année de publication : 2019 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] données localisées 3D
[Termes IGN] prise en compte du contexte
[Termes IGN] représentation multiple
[Termes IGN] scène
[Termes IGN] scène intérieure
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning architectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information in-between objects efficiently. Inspired by a global feature aggregation algorithm designed for images (Zhao et al., 2017), we propose a 3D pyramid module to enrich pointwise features with multi-scale contextual information. Our module can be easily coupled with 3D semantic segmentation methods operating on 3D point clouds. We evaluated our method on three large scale datasets with four baseline models. Experimental results show that the use of enriched features brings significant improvements to the semantic segmentation of indoor and outdoor scenes. Numéro de notice : A2019-271 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.010 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93089
in ISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 246 - 258[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Semantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
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Titre : Semantic segmentation of road furniture in mobile laser scanning data Type de document : Article/Communication Auteurs : Fashuai Li, Auteur ; Matti Lehtomäki, Auteur ; Sander J. Oude Elberink, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 98 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] mobilier urbain
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Road furniture recognition has become a prevalent issue in the past few years because of its great importance in smart cities and autonomous driving. Previous research has especially focussed on pole-like road furniture, such as traffic signs and lamp posts. Published methods have mainly classified road furniture as individual objects. However, most road furniture consists of a combination of classes, such as a traffic sign mounted on a street light pole. To tackle this problem, we propose a framework to interpret road furniture at a more detailed level. Instead of being interpreted as single objects, mobile laser scanning data of road furniture is decomposed in elements individually labelled as poles, and objects attached to them, such as, street lights, traffic signs and traffic lights. In our framework, we first detect road furniture from unorganised mobile laser scanning point clouds. Then detected road furniture is decomposed into poles and attachments (e.g. traffic signs). In the interpretation stage, we extract a set of features to classify the attachments by utilising a knowledge-driven method and four representative types of machine learning classifiers, which are random forest, support vector machine, Gaussian mixture model and naïve Bayes, to explore the optimal method. The designed features are the unary features of attachments and the spatial relations between poles and their attachments. Two experimental test sites in Enschede dataset and Saunalahti dataset were applied, and Saunalahti dataset was collected in two different epochs. In the experimental results, the random forest classifier outperforms the other methods, and the overall accuracy acquired is higher than 80% in Enschede test site and higher than 90% in both Saunalahti epochs. The designed features play an important role in the interpretation of road furniture. The results of two epochs in the same area prove the high reliability of our framework and demonstrate that our method achieves good transferability with an accuracy over 90% through employing the training data of one epoch to test the data in another epoch. Numéro de notice : A2019-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.001 Date de publication en ligne : 08/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93081
in ISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 98 - 113[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)
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Titre : Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data Type de document : Article/Communication Auteurs : Joris Ravaglia, Auteur ; Richard A. Fournier, Auteur ; Alexandra Bac, Auteur ; Cédric Vega , Auteur ; Jean-François Côté, Auteur ; Alexandre Piboule, Auteur ; Ulysse Rémillard, Auteur
Année de publication : 2019 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] Canada
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuillu
[Termes IGN] France (administrative)
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Pinophyta
[Termes IGN] semis de points
[Termes IGN] transformation de Hough
[Termes IGN] volume en boisRésumé : (auteur) Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. Numéro de notice : A2019-337 Affiliation des auteurs : LIF+Ext (2012-2019) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f10070599 Date de publication en ligne : 18/07/2019 En ligne : https://hal.science/hal-03325416v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93367
in Forests > vol 10 n° 7 (July 2019) . - 19 p.[article]Empirical 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)
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Titre : Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners Type de document : Article/Communication Auteurs : Tomislav Medic, Auteur ; Christoph Holst, Auteur ; Jannik Janssen, Auteur ; Heiner Kuhlmann, Auteur Année de publication : 2019 Article en page(s) : pp 179 – 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] centroïde
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage d'instrument
[Termes IGN] incertitude de mesurage
[Termes IGN] métrologie dimensionelle
[Termes IGN] modèle stochastique
[Termes IGN] semis de points
[Termes IGN] télémètre laser terrestreRésumé : (auteur) The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior. Numéro de notice : A2019-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0032 Date de publication en ligne : 22/03/2019 En ligne : https://doi.org/10.1515/jag-2018-0032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93119
in Journal of applied geodesy > vol 13 n° 3 (July 2019) . - pp 179 – 197[article]Innovations 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)
PermalinkLandslide monitoring analysis of single-frequency BDS/GPS combined positioning with constraints on deformation characteristics / Dongwei Qiu in Survey review, vol 51 n° 367 (July 2019)
PermalinkLarge scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain / Convadonga Prendes in iForest, biogeosciences and forestry, vol 12 n° 4 (July 2019)
PermalinkMonitoring the structure of forest restoration plantations with a drone-lidar system / D.R.A. Almeida in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)
PermalinkShadow detection and correction using a combined 3D GIS and image processing approach / Safa Ridene in Revue internationale de géomatique, vol 29 n° 3 - 4 (juillet - décembre 2019)
PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
PermalinkUsing LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
PermalinkDemonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)
PermalinkAutomatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)
PermalinkCombining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest / Angela Blázquez-Casado in Annals of Forest Science, vol 76 n° 2 (June 2019)
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