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Vegetation stratum occupancy prediction from airborne LiDAR 3D point clouds / Ekaterina Kalinicheva (2021)
Titre : Vegetation stratum occupancy prediction from airborne LiDAR 3D point clouds Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva , Auteur ; Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : 1-Pas de projet / Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Importance : pp 41 - 43 Note générale : Data sets in https://zenodo.org/badge/DOI/10.5281/zenodo.5555758.svg Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] capteur aérien
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de pointsRésumé : (auteur) [introduction] Estimating the structure of vegetation is a crucial first step for many environmental and ecological applications (Daubenmire 1956). In particular, pasture land management requires estimating the occupancy of the different vegetation strata within agricultural parcels. This is a time-consuming undertaking, often performed with in situ ocular approximate measurements. Nowadays, airborne platforms allow public and private actors to gather high accuracy geometric and radiometric data over large areas (Chen 2007). Bolstered by the compelling improvements (Guo et al., 2020) and increased accessibility of deep learning for 3D point clouds, we propose a 3D deep learning method to estimate the occupancy of different vegetation strata from airborne LiDAR and camera sensors. Our method predicts raster occupancy maps for three vegetation strata (lower, medium, and higher) from 3D point clouds. Our training scheme allows our network to only be supervised with aggregated occupancy values at the plot level, which are easier to produce than point or pixel-level annotations. We also propose to use priors on the stratum elevation and the occupancy maps to improve the legibility and interpretability of the resulting maps. Numéro de notice : C2021-032 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1909 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98996 Adjusting the regular network of squares resolution to the digital terrain model surface shape / Dariusz Gościewski in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
[article]
Titre : Adjusting the regular network of squares resolution to the digital terrain model surface shape Type de document : Article/Communication Auteurs : Dariusz Gościewski, Auteur ; Małgorzata Gerus-Gościewska, Auteur Année de publication : 2020 Article en page(s) : n° 761 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] interpolation
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] morphologie
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) A regular network of squares is formed by points uniformly distributed (mostly in the square corners) over the surface that is represented by the network. Each point (node) of the network has specified coordinates (X and Y) with a fixed constant distance between them. The third coordinate in a node (H) is determined by the application of interpolation based on the points distributed (usually dispersed as a point cloud e.g., from LiDAR) over the surface of the area surrounding the node. The regular network of squares formed in this manner allows the representation of a digital terrain model (DTM) to be performed in spatial information systems (SIP, GIS). The main problem that arises during the construction of such a network is the proper determination of its resolution (the base distance between the coordinates X and Y) depending on the topography. This article presents a method of the regular network of squares resolution determination depending on the morphological shape of the terrain surface. Following the application of the procedures being described, a differently shaped terrain is assigned various network densities. This enables the minimisation of inaccuracies of the surface model being formed. Consequently, a regular network of squares is formed with different base square sizes, which is adjusted with its resolution to the morphology of the surface it describes. Such operations allow the terrain model accuracy to be maintained over the entire area while reducing the number of points stored in the DTM database to the minimum. Numéro de notice : A2020-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120761 Date de publication en ligne : 20/12/2020 En ligne : https://doi.org/10.3390/ijgi9120761 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96952
in ISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 761[article]Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)
[article]
Titre : Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique Type de document : Article/Communication Auteurs : Maxime Lafleur, Auteur ; Elliot Mugner, Auteur ; Rabine Keyetieu-Nlowe, Auteur ; Nicolas Seube, Auteur Année de publication : 2020 Article en page(s) : pp 25 -32 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auscultation d'ouvrage
[Termes IGN] barrage
[Termes IGN] base de données localisées 3D
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] drone
[Termes IGN] exactitude des données
[Termes IGN] filtrage du bruit
[Termes IGN] géoréférencement
[Termes IGN] Haute-Loire (43)
[Termes IGN] précision des données
[Termes IGN] semis de points
[Termes IGN] sol nuRésumé : (Auteur) Le levé LiDAR présenté dans cet article a été effectué dans le cadre d’une mission d’évaluation de la chaîne de traitement mdInfinity, appliquée à des données acquises par un système drone LiDAR Microdrones. Les différents outils qui constituent cette chaîne de traitement ont été développés et intégrés sur la plateforme de traitement mdInfinity dans une version particulièrement adaptée au système de levé utilisé pour cette mission. Le site utilisé pour cette évaluation est le barrage de Labrioulette (Haute-Garonne), infrastructure située sur la Garonne et exploitée par EDF. Cette zone contient de nombreux éléments sur lesquels la précision et l’exactitude des données LiDAR est primordiale afin d’obtenir un nuage de point exploitable ; notamment la complexité structurelle du barrage (figure 1), les zones sous couvert végétal dense, l’aire de transformation électrique, etc. Pour cette raison, en plus de confirmer la bonne interopérabilité des systèmes LiDAR Microdrones avec les outils de traitement mdInfinity, nous avons tiré profit de cette acquisition pour évaluer les performances de nos algorithmes. Numéro de notice : A2020-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96662
in XYZ > n° 165 (décembre 2020) . - pp 25 -32[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2020041 RAB Revue Centre de documentation En réserve L003 Disponible MS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
[article]
Titre : MS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds Type de document : Article/Communication Auteurs : Haifeng Luo, Auteur ; Chongcheng Chen, Auteur ; Lina Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8301 - 8315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] cognition
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] représentation multiple
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Semantic segmentation is one of the fundamental tasks in understanding and applying urban scene point clouds. Recently, deep learning has been introduced to the field of point cloud processing. However, compared to images that are characterized by their regular data structure, a point cloud is a set of unordered points, which makes semantic segmentation a challenge. Consequently, the existing deep learning methods for semantic segmentation of point cloud achieve less success than those applied to images. In this article, we propose a novel method for urban scene point cloud semantic segmentation using deep learning. First, we use homogeneous supervoxels to reorganize raw point clouds to effectively reduce the computational complexity and improve the nonuniform distribution. Then, we use supervoxels as basic processing units, which can further expand receptive fields to obtain more descriptive contexts. Next, a sparse autoencoder (SAE) is presented for feature embedding representations of the supervoxels. Subsequently, we propose a regional relation feature reasoning module (RRFRM) inspired by relation reasoning network and design a multiscale regional relation feature segmentation network (MS-RRFSegNet) based on the RRFRM to semantically label supervoxels. Finally, the supervoxel-level inferences are transformed into point-level fine-grained predictions. The proposed framework is evaluated in two open benchmarks (Paris-Lille-3D and Semantic3D). The evaluation results show that the proposed method achieves competitive overall performance and outperforms other related approaches in several object categories. An implementation of our method is available at: https://github.com/HiphonL/MS_RRFSegNet . Numéro de notice : A2020-738 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2985695 Date de publication en ligne : 28/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2985695 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96363
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8301 - 8315[article]Remote sensing in urban planning: Contributions towards ecologically sound policies? / Thilo Wellmann in Landscape and Urban Planning, vol 204 (December 2020)
[article]
Titre : Remote sensing in urban planning: Contributions towards ecologically sound policies? Type de document : Article/Communication Auteurs : Thilo Wellmann, Auteur ; Angela Lausch, Auteur ; Erik Andersson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103921 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écologie
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] littérature
[Termes IGN] orthoimage
[Termes IGN] planification urbaine
[Termes IGN] risque environnemental
[Termes IGN] semis de points
[Termes IGN] service écosystémique
[Termes IGN] télédétection
[Termes IGN] urbanismeRésumé : (auteur) Remote sensing has evolved to become a key tool for various fields of environmental analysis, thus actively informing policy across areas and domains. To evaluate the degree to which remote sensing is contributing to the science of ecologically-oriented urban planning, we carried out a systematic literature review using the SCOPUS database, searching for articles integrating knowledge in urban planning, remote sensing and ecology. We reviewed 186 articles, analysing various issues in urban environments worldwide. Key findings include that the level of integration between the three disciplines is limited, with only 12% of the papers fully integrating ecology, remote sensing and planning while 24% of the studies use specific methods from one domain only. The vast majority of studies is oriented towards contributing to the knowledge base or monitoring the impacts of existing policies. Few studies are directly policy relevant by either contributing to direct issues in planning and making specific design suggestions or evaluations. The accessibility of the scientific findings remains limited, as the majority of journal articles are not open access and proprietary software and data are frequently used. To overcome these issues, we suggest three future avenues for science as well as three potential entry points for remote sensing into applied urban planning. By doing so, remote sensing data could become a vital tool actively contributing to policies, civil engagement and concrete planning measures by providing independent and cost effective environmental analyses. Numéro de notice : A2020-734 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.landurbplan.2020.103921 Date de publication en ligne : 27/08/2020 En ligne : https://doi.org/10.1016/j.landurbplan.2020.103921 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96348
in Landscape and Urban Planning > vol 204 (December 2020) . - n° 103921[article]The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland / Ranjith Gopalakrishnan in Annals of Forest Science, vol 77 n° 4 (December 2020)PermalinkActive and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkBuilding change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkBuilding facade reconstruction using crowd-sourced photos and two-dimensional maps / Wu Jie in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)PermalinkEffects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkIndoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkIs field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest / Luka Jurjević in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkTopographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkAssessing the effects of thinning on stem growth allocation of individual Scots pine trees / Ninni Saarinen in Forest ecology and management, vol 474 ([15/10/2020])PermalinkComparing features of single and multi-photon lidar in boreal forests / Xiaowei Yu in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkGoing to the Finnish line / Hannu Heinonen in GEO: Geoconnexion international, vol 19 n° 6 (October 2020)PermalinkHierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkA LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)PermalinkSee 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)PermalinkThe 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)PermalinkTowards 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)PermalinkTree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkWeighted 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)Permalink