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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)
[article]
Titre : Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data Type de document : Article/Communication Auteurs : Lukas Winiwarter, Auteur ; Katharina Anders, Auteur ; Daniel Schröder, Auteur ; Bernhard Höfle, Auteur Année de publication : 2022 Article en page(s) : pp 79 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtre de Kalman
[Termes IGN] modèle de simulation
[Termes IGN] scène 3D
[Termes IGN] scène virtuelle
[Termes IGN] semis de points
[Termes IGN] Tyrol (Autriche)Résumé : (autuer) Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality. Numéro de notice : A2022-437 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-79-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100746
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 79 - 86[article]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)
[article]
Titre : A voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results Type de document : Article/Communication Auteurs : N. Homainejad, Auteur ; Sisi Zlatanova, Auteur ; Norbert Pfeifer, Auteur Année de publication : 2022 Article en page(s) : pp 697 - 704 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] incendie de forêt
[Termes IGN] lande
[Termes IGN] modélisation 3D
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] segmentation en régions
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (auteur) Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees. Numéro de notice : A2022-436 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-3-2022-697-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100783
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 697 - 704[article]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)
[article]
Titre : 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation Type de document : Article/Communication Auteurs : Heyang Thomas Li, Auteur ; Zachary Todd, Auteur ; Nikolas Bielski, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1759 - 1774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espace image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] route
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] signalisation routièreRésumé : (auteur) The classification and extraction of road markings and lanes are of critical importance to infrastructure assessment, planning and road safety. We present a pipeline for the accurate segmentation and extraction of rural road surface objects in 3D lidar point-cloud, as well as a method to extract geometric parameters belonging to tar seal. To decrease the computational resources needed, the point-clouds were aggregated into a 2D image space before being transformed using affine transformations. The Mask R-CNN algorithm is then applied to the transformed image space to localize, segment and classify the road objects. The segmentation results for road surfaces and markings can then be used for geometric parameter estimation such as road widths estimation, while the segmentation results show that the efficacy of the existing Mask R-CNN to segment needle-type objects is improved by our proposed transformations. Numéro de notice : A2022-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02103-8 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02103-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100627
in The Visual Computer > vol 38 n° 5 (May 2022) . - pp 1759 - 1774[article]City3D: Large-scale building reconstruction from airborne LiDAR point clouds / Jin Huang in Remote sensing, vol 14 n° 9 (May-1 2022)
[article]
Titre : City3D: Large-scale building reconstruction from airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Jin Huang, Auteur ; Jantien E. Stoter, Auteur ; Ravi Peters, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] empreinte
[Termes IGN] mur
[Termes IGN] polygonale
[Termes IGN] primitive géométrique
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] Triangular Regular Network
[Termes IGN] triangulation de DelaunayRésumé : (auteur) We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications. Numéro de notice : A2022-387 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14092254 Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.3390/rs14092254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100667
in Remote sensing > vol 14 n° 9 (May-1 2022) . - n° 2254[article]Efficient convolutional neural architecture search for LiDAR DSM classification / Aili Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)
[article]
Titre : Efficient convolutional neural architecture search for LiDAR DSM classification Type de document : Article/Communication Auteurs : Aili Wang, Auteur ; Dong Xue, Auteur ; Haibin Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703317 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle de transfert radiatif
[Termes IGN] modèle numérique de surface
[Termes IGN] précision de la classification
[Termes IGN] semis de pointsRésumé : (auteur) Light detection and ranging (LiDAR) data provide rich elevation information, so it plays an irreplaceable role in ground object classification. Recently, convolutional neural networks (CNNs) have shown excellent performance in LiDAR digital surface models (DSMs) classification. However, the architecture of CNN model relies heavily on manual design, so it has great limitations. In addition, different sensors capture LiDAR datasets with different properties, so the model should be designed to suit for different datasets, which further increases the workload of architecture design. Therefore, this article proposes a method of automatic design of LiDAR DSM classification model. First, attention mechanism is introduced into search space to improve the feature extraction capability of the model. Then, a gradient-based search strategy is used to obtain the optimal architecture from this search space. Second, a learning rate adjustment strategy is proposed to reduce the time spent in the search stage and evaluation stage to improve the classification accuracy of the model. Finally, a regularization scheme is introduced to enhance the robustness of the model and avoid overfitting. Experimental results on three public LiDAR datasets (Bayview Park, Recology, and Houston) obtained from different sensors show that the proposed neural architecture search method achieves the impressive classification performance compared to several state-of-the-art classification methods and improves the classification accuracy under the condition of limited training samples. Numéro de notice : A2022-408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3171520 Date de publication en ligne : 02/05/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3171520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100742
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 5 (May 2022) . - n° 5703317[article]Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads / Raul de Paula Pires in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkWeakly supervised semantic segmentation of airborne laser scanning point clouds / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkAssessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkCharacterizing stream morphological features important for fish habitat using airborne laser scanning data / Spencer Dakin Kuiper in Remote sensing of environment, vol 272 (April 2022)PermalinkComparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data / Andras Balazs in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)PermalinkData assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)PermalinkDeep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkEstimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models / Ana de Lera Garrido in Silva fennica, vol 56 n° 2 (April 2022)PermalinkA graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkAssessing the dependencies of scots pine (Pinus sylvestris L.) structural characteristics and internal wood property variation / Ville Kankare in Forests, vol 13 n° 3 (March 2022)PermalinkA cost-effective method for reconstructing city-building 3D models from sparse Lidar point clouds / Marek Kulawiak in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkÉvaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine / Maxime Mérizette in XYZ, n° 170 (mars 2022)PermalinkLiDAR-based method for analysing landmark visibility to pedestrians in cities: case study in Kraków, Poland / Krystian Pyka in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkTowards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) / Langning Huo in Remote sensing of environment, vol 270 (March 2022)PermalinkPCEDNet: a lightweight neural network for fast and interactive edge detection in 3D point clouds / Chems-Eddine Himeur in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)PermalinkPlanning of commercial thinnings using machine learning and airborne Lidar data / Tauri Arumäe in Forests, vol 13 n° 2 (February 2022)PermalinkQuantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data / Tianyu Hu in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkThree-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkAnalyse haute résolution de la morphologie des paysages et des processus à partir de LiDAR aéroporté répété et simulation hydraulique / Thomas Bernard (2022)PermalinkA comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories / Janne Raty in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkContributions of multi-temporal airborne LiDAR data to mapping carbon stocks and fluxes in tropical forests / Claudia Milena Huertas Garcia (2022)PermalinkPermalinkGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkRobust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)PermalinkScaling up and evaluating surface reconstruction from point clouds of open scenes / Yanis Marchand (2022)PermalinkApplication of a hand-held LiDAR scanner for the urban cadastral detail survey in digitized cadastral area of Taiwan urban city / Shih-Hong Chio in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkModeling post-logging height growth of black spruce-dominated boreal forests by combining airborne LiDAR and time since harvest maps / Batistin Bour in Forest ecology and management, vol 502 (December-15 2021)PermalinkAssessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones / Lonesome Malambo in Remote sensing of environment, vol 266 (December 2021)PermalinkAtelier LiDAR mobile & aéroporté / Pierre Assali in XYZ, n° 169 (décembre 2021)PermalinkAutomatic registration of mobile mapping system Lidar points and panoramic-image sequences by relative orientation model / Ningning Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)PermalinkDetection of periodic displacements of shell structures with edges using spline surfaces, meshes and point clouds / Grzegorz Lenda in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)PermalinkPermalinkEstimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkA hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds / Yetao Yang in Computers & geosciences, vol 157 (December 2021)PermalinkLe Mont-Blanc mesuré au LiDAR héliporté / Mathieu Peyréga in XYZ, n° 169 (décembre 2021)PermalinkPoint clouds for use in Building Information Models (BIM) / Robert Klinc in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)PermalinkUtility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data / Siamak Talebi Nahr in Photogrammetric record, Vol 36 n° 176 (December 2021)PermalinkForest structural complexity tool: An open source, fully-automated tool for measuring forest point clouds / Sean Krisanski in Remote sensing, vol 13 n° 22 (November-2 2021)Permalink