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An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)
[article]
Titre : An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery Type de document : Article/Communication Auteurs : Luis Angel Ruiz, Auteur ; Jorge Abel Recio, Auteur ; Pablo Crespo-Peremarch, Auteur ; Marta Sapena Moll, Auteur Année de publication : 2018 Article en page(s) : pp 443 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre de décision
[Termes IGN] biomasse (combustible)
[Termes IGN] carte forestière
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[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] forêt méditerranéenne
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. Numéro de notice : A2018-140 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265595 Date de publication en ligne : 28/11/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89690
in Geocarto international > vol 33 n° 5 (May 2018) . - pp 443 - 457[article]Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network Type de document : Article/Communication Auteurs : Ruibin Zhao, Auteur ; Mingyong Pang, Auteur ; Jidong Wang, Auteur Année de publication : 2018 Article en page(s) : pp 960 - 979 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. Numéro de notice : A2018-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1431840 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1431840 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89861
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 960 - 979[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud / Cigdem Serifoglu Yilmaz in Geocarto international, vol 33 n° 5 (May 2018)
[article]
Titre : Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud Type de document : Article/Communication Auteurs : Cigdem Serifoglu Yilmaz, Auteur ; Oguz Gungor, Auteur Année de publication : 2018 Article en page(s) : pp 522 - 537 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] interpolation
[Termes IGN] Matlab
[Termes IGN] modèle numérique de terrain
[Termes IGN] performance
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular Network
[Termes IGN] universitéRésumé : (Auteur) Ground filtering algorithms mainly focus on filtering LiDAR (Light Detection and Ranging) point clouds owing to their intrinsic characteristics to classify ground and non-ground points. However, the acquisition and processing of LiDAR data is still costly. Compared to LiDAR technology, UAVs (Unmanned Aerial Vehicle) are cheap and easy to use. In this study, the performances of five widely used ground filtering algorithms (Progressive Morphological 1D/2D, Maximum Local Slope, Elevation Threshold with Expand Window, and Adaptive TIN) were investigated by conducting qualitative and quantitative evaluations on UAV-based point clouds. Evaluation results indicated that the Adaptive TIN algorithm presented the best performance. The result of the Adaptive TIN algorithm was interpolated by using a MATLAB script to generate the DTM (Digital Terrain Model). Field measurements indicated that using UAV-based point clouds may be a reasonable alternative for LiDAR data, depending on the characteristics of the study area. Numéro de notice : A2018-141 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265599 Date de publication en ligne : 07/12/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265599 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89691
in Geocarto international > vol 33 n° 5 (May 2018) . - pp 522 - 537[article]From point cloud to BIM: an integrated workflow for documentation, research and modelling of architectural heritage / C. Rodríguez-Moreno in Survey review, vol 50 n° 360 (May 2018)
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Titre : From point cloud to BIM: an integrated workflow for documentation, research and modelling of architectural heritage Type de document : Article/Communication Auteurs : C. Rodríguez-Moreno, Auteur ; J.F. Reinoso-Gordo, Auteur ; E. Rivas-López, Auteur ; A. Gómez-Blanco, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 212 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] église
[Termes IGN] Grenade
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] patrimoine immobilier
[Termes IGN] semis de pointsRésumé : (auteur) Heritage buildings traditionally have been tackled from several points of view: architectonic features, architectural style, archaeology, history, etc. Documents derived from those studies were classified and put together to form the documentation for consultation, taking decisions about its conservation, and restoration. Such a document organisation has some inconveniences: objects composing the building were treated as isolated objects not related to its adjacent objects. Nowadays technology may help to discover the relationship between architectural objects forming heritage buildings. The tool that makes it possible to include functionality in architectural objects is BIM (Building Information Modelling). In this paper, the historical evolution of Saint Jeromés Church in Baza will be analysed and stored in a functional model which includes geometry and its current state. We propose a procedure for building the BIM through its historical roots and evolution to be included in each remarkable object modelled from the point cloud surveyed. Numéro de notice : A2018-183 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/00396265.2016.1259719 Date de publication en ligne : 02/12/2016 En ligne : https://doi.org/10.1080/00396265.2016.1259719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89827
in Survey review > vol 50 n° 360 (May 2018) . - pp 212 - 231[article]Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)
[article]
Titre : Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net Type de document : Article/Communication Auteurs : Timo Hackel, Auteur ; Jan Dirk Wegner, Auteur ; Nikolay Savinov, Auteur ; Lubor Ladicky, Auteur ; Konrad Schindler, Auteur ; Marc Pollefeys, Auteur Année de publication : 2018 Article en page(s) : pp 297 - 308 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] état de l'art
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) In this paper, we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labeled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labeling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-) data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains eight semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields, and castles. We describe our labeling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labeled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D.net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general. Numéro de notice : A2018-162 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.5.297 Date de publication en ligne : 01/05/2018 En ligne : https://doi.org/10.14358/PERS.84.5.297 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89795
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 5 (mai 2018) . - pp 297 - 308[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2018051 RAB Revue Centre de documentation En réserve L003 Disponible LiDAR, a technology to assist with smart cities and climate change resilience: a case study in an urban metropolis / Ryan Garnett in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)PermalinkEffects of terrain slope and aspect on the error of ALS-based predictions of forest attributes / Hans Ole Ørka in Forestry, an international journal of forest research, vol 91 n° 2 (April 2018)PermalinkReal-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras / David Joseph Tan in International journal of computer vision, vol 126 n° 2-4 (April 2018)PermalinkRevue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile / Sylvie Daniel in Geomatica, vol 72 n° 1 (March 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUse of LiDAR for calculating solar irradiance on roofs and façades of buildings at city scale: Methodology, validation, and analysis / Liang Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUsing terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach / Stéphane Momo Takoudjou in Methods in ecology and evolution, vol 9 n° 4 (April 2018)Permalink3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis / Benjamin Herfort in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)Permalink3D visibility analysis indicating quantitative and qualitative aspects of the visible space / D. Golub in Survey review, vol 50 n° 359 (March 2018)PermalinkAnalyse du risque végétation dans les emprises ferroviaires à partir de données LiDAR acquises par drones / Luc Perrin in XYZ, n° 154 (mars - mai 2018)PermalinkImage classification-based ground filtering of point clouds extracted from UAV-based aerial photos / Volkan Yilmaz in Geocarto international, vol 33 n° 3 (March 2018)PermalinkImportant LiDAR metrics for discriminating forest tree species in Central Europe / Yifang Shi in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkLarge off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkLittoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)PermalinkMultisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkPredicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkRobust interpolation of DEMs from lidar-derived elevation data / Chuanfa Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkValue of airborne laser scanning and digital aerial photogrammetry data in forest decision making / Annika S. Kangas in Silva fennica, vol 52 n° 1 ([01/02/2018])PermalinkAirborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAssessing forest windthrow damage using single-date, post-event airborne laser scanning data / Gherardo Chirici in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAutomated extraction of hydrographically corrected contours for the conterminous United States: the US Geological Survey US Topo product / Samantha T. 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Palancz in Survey review, vol 49 n° 357 (December 2017)PermalinkArea-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds / Paweł Hawryło in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)PermalinkBuilding extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm / Saeid Parsian in Geomatica, vol 71 n° 4 (December 2017)PermalinkLow-cost warning system for the monitoring of the Corinth Canal / George Hloupis in Applied geomatics, vol 9 n° 4 (December 2017)PermalinkModélisation d'un oppidum sous couvert végétal dense, en Eure-et-Loir, par un LiDAR aéroporté par drone / Isabelle Heitz in XYZ, n° 153 (décembre 2017 - février 2018)PermalinkPairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game / Dawei Zai in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkRemotely sensed forest habitat structures improve regional species conservation / Christian Reichsteiner in Remote sensing in ecology and conservation, vol 3 n° 4 (December 2017)Permalink