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Multitemporal 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)
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Titre : Multitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data Type de document : Article/Communication Auteurs : Tomás Fernández, Auteur ; José Luis Pérez-García, Auteur ; José Miguel Gómez-López, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 30 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] Andalousie
[Termes IGN] données lidar
[Termes IGN] données publiques
[Termes IGN] érosion
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] Olea europaea
[Termes IGN] orthophotographie
[Termes IGN] point d'appui
[Termes IGN] précipitation
[Termes IGN] ravin
[Termes IGN] semis de pointsRésumé : (auteur) Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape. Numéro de notice : A2020-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040260 Date de publication en ligne : 19/04/2020 En ligne : https://doi.org/10.3390/ijgi9040260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95029
in ISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 30 p.[article]Using 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)
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Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse multiéchelle
[Termes IGN] apprentissage profond
[Termes IGN] approche hiérarchique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] modélisation 3D
[Termes IGN] Oakland (Californie)
[Termes IGN] régression
[Termes IGN] semis de pointsRésumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 Date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711
in International journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680[article]How 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)
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Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auscultation topographique
[Termes IGN] diamètre des arbres
[Termes IGN] données dendrométriques
[Termes IGN] données lidar
[Termes IGN] feuillu
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat-OLI
[Termes IGN] inventaire forestier local
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] modèle d'erreur
[Termes IGN] Pinophyta
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] surface forestière
[Termes IGN] télémètre laser aéroportéRésumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 Date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535
in International Journal of Remote Sensing IJRS > vol 41 n° 12 (20 - 30 March 2020) . - pp 4551 - 4569[article]3D laser scanning of the natural caves: Example of Škocjanske jame / Richard Walters in Geodetski vestnik, Vol 64 n° 1 (March - May 2020)
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Titre : 3D laser scanning of the natural caves: Example of Škocjanske jame Type de document : Article/Communication Auteurs : Richard Walters, Auteur ; Nadja Zupan Hajna, Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] 3DReshaper
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] grotte
[Termes IGN] image captée par drone
[Termes IGN] instrument embarqué
[Termes IGN] lasergrammétrie
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] site historique
[Termes IGN] SlovénieRésumé : (auteur) In this article, we present issues arising from Terrestrial Laser Scanning of large natural caves using the example of Škocjan Caves, a UNESCO World Heritage Site. Regarding pre-existing tachymetric survey of the passages and volumes calculated from them, the scanning of such a large cave was an even bigger challenge for the team. The cave of almost 6 km long passages with dimensions approx. 30 m x 40 m and max. heights up to 145 m, was scanned from 370 stations. Process of surveying the cave, involves establishing scanner positions through the cave, where scans will overlap, in a progressive route and once back on the surface, collecting, cleaning and stitching the scans into a point cloud 3D model. A total of 8.3 billion points were captured and 2,600 high-resolution photos taken. With Reigl’s RiSCAN Pro software, a point cloud model was registered and then exported to Hexagon’s 3D Reshaper to create a full surface model from which all measurements and calculations were made. Additionally, data acquisition using a camera on an unmanned airborne vehicle was used. By photogrammetric approach, digital terrain model of a surface was built and then tied to the cave model within 3D Reshaper. The resulting high resolution - point cloud model may be used for various purposes such as: volume calculations, detection of geological and speleogenetical features, etc. With a volume of 2.55 million cubic metres, Martel’s Chamber is confirmed to be the 11th largest cave chamber in the world at the moment. Numéro de notice : A2020-275 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.01.89-103 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.15292/geodetski-vestnik.2020.01.89-103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96088
in Geodetski vestnik > Vol 64 n° 1 (March - May 2020) . - 15 p.[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2020011 RAB Revue Centre de documentation En réserve L003 Disponible An improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)
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Titre : An improved RANSAC algorithm for extracting roof planes from airborne lidar data Type de document : Article/Communication Auteurs : Sibel Canaz Sevgen, Auteur ; Fevzi Karsli, Auteur Année de publication : 2020 Article en page(s) : pp 40 - 57 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Algorithmique
[Termes IGN] bord décollé (toit)
[Termes IGN] contrôle qualité
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Ransac (algorithme)
[Termes IGN] segmentation en régions
[Termes IGN] semis de pointsRésumé : (Auteur) The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus (RANSAC) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐RANSAC) algorithm by removing points that do not belong to the roof plane. I‐RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC. Numéro de notice : A2020-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12296 Date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1111/phor.12296 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94815
in Photogrammetric record > vol 35 n° 169 (March 2020) . - pp 40 - 57[article]Classification 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)
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