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Modeling merchantable wood volume using airborne LiDAR metrics and historical forest inventory plots at a provincial scale / Antoine Leboeuf in Forests, vol 13 n° 7 (July 2022)
[article]
Titre : Modeling merchantable wood volume using airborne LiDAR metrics and historical forest inventory plots at a provincial scale Type de document : Article/Communication Auteurs : Antoine Leboeuf, Auteur ; Martin Riopel, Auteur ; Dave Munger, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 985 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] densité du bois
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] parcelle forestière
[Termes IGN] placette d'échantillonnage
[Termes IGN] Québec (Canada)
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) So far, large-scale projects aiming to map forest attributes using aerial LiDAR data have been developed using ground sample plots acquired synchronously with LiDAR. No large projects have been developed using aerial LiDAR acquired independent of ground sample plot datasets. The goal of this study was to develop and validate large-scale parametric merchantable wood volume estimation models using existing historical ground sample plots. The models can be applied to large LiDAR datasets to map merchantable wood volume as a 10 × 10 m raster. The study demonstrated that a relative density index (RDI) based on a self-thinning equation and dominant height were suitable variables that can be calculated both for ground sample plots and LiDAR datasets. The resulting volume raster showed sound accuracy rates when compared to validation zones: R², 82.25%; RMSE, 13.7 m3/ha; and bias, −4.09 m3/ha. The results show that ground sample plot datasets acquired synchronously with LiDAR can be used to calculate the RDI and dominant height. These variables can consequently be used to map forest attributes over a large area with a high level of accuracy, thus not requiring the implementation of new costly sample plots. Numéro de notice : A2022-547 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13070985 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3390/f13070985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101132
in Forests > vol 13 n° 7 (July 2022) . - n° 985[article]A participatory trail web map based on open source technologies / Joshua Gore in International journal of cartography, vol 8 n° 2 (July 2022)
[article]
Titre : A participatory trail web map based on open source technologies Type de document : Article/Communication Auteurs : Joshua Gore, Auteur ; Stefan Peters, Auteur ; Delene Weber, Auteur Année de publication : 2022 Article en page(s) : pp 223 - 242 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] Australie méridionale (Australie)
[Termes IGN] base de données localisées
[Termes IGN] carte interactive
[Termes IGN] chemin rural
[Termes IGN] données localisées des bénévoles
[Termes IGN] données localisées libres
[Termes IGN] évaluation des données
[Termes IGN] qualité des données
[Termes IGN] randonnée
[Termes IGN] web mappingRésumé : (auteur) Interactive maps can be an important marketing tool for disseminating information about long distance walking trails and a way to value add to the recreation experience. Providing participatory functionality to maps by allowing users to share new information or refine existing information can greatly enhance user interest and improve the product. This research uses open source front and back end technologies to develop a single page, database driven participatory web map application for the Walk the Yorke Trail in South Australia. The development is based on and assessed through a user-centred design approach. The prototype participatory web map is assessed by cartographic experts, trail managers, and trail users. These evaluations indicate the validity of the design directions taken but highlight the need for information quality and quantity when encouraging participation from knowledgeable trail users, and the need for tools to ensure the continuing quality of further submitted information. Numéro de notice : A2022-921 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1840320 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/23729333.2020.1840320 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102461
in International journal of cartography > vol 8 n° 2 (July 2022) . - pp 223 - 242[article]Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)
[article]
Titre : Polyline simplification based on the artificial neural network with constraints of generalization knowledge Type de document : Article/Communication Auteurs : Jiawei Du, Auteur ; Jichong Yin, Auteur ; Chengyi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 313 - 337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] descripteur
[Termes IGN] données maillées
[Termes IGN] données vectorielles
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] polyligne
[Termes IGN] programmation par contraintes
[Termes IGN] réseau neuronal artificiel
[Termes IGN] simplification de contour
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles. Numéro de notice : A2022-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2021.2013944 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1080/15230406.2021.2013944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100885
in Cartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 313 - 337[article]Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)
[article]
Titre : Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading Type de document : Article/Communication Auteurs : Štefan Kohek, Auteur ; Borut Žalik, Auteur ; Damjan Strnad, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102844 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de sensibilité
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] dissymétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] houppier
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation de la forêt
[Termes IGN] ombre
[Termes IGN] semis de points
[Termes IGN] SlovénieRésumé : (auteur) Reliable forest growth forecasting requires detailed tree data for forest simulation, while manual on-site collection of relevant data is work-intensive and unfeasible in larger forests. This paper proposes a complete methodology for fully automated forest growth simulation that relies primarily on airborne topographic Light Detection And Ranging (LiDAR) point clouds of individual trees. The proposed method estimates tree parameters and performs growth of individual trees based on an individual-based forest growth simulator, named BWINPro. In addition, competition and detailed asymmetric tree crown growth are modeled regarding the shading of tree crowns, which is estimated from the surrounding environment and neighbor trees. The result of the proposed approach is a new point cloud for subsequent analyses. The proposed method was validated by comparing canopy height models derived from the point clouds of the simulated trees with canopy height models derived from more recent ground truth point clouds. The results demonstrate the efficacy of the proposed method which achieves a 9.4% higher accuracy than the averaged linear regression model and, in the case of datasets with more distinct self-standing trees, where a tree crown boundary plays major role, a 4.1% higher accuracy than the directly fitted linear regression model. Numéro de notice : A2022-552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102844 Date de publication en ligne : 04/06/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101156
in International journal of applied Earth observation and geoinformation > vol 111 (July 2022) . - n° 102844[article]Spatial-temporal attentive LSTM for vehicle-trajectory prediction / Rui Jiang in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)
[article]
Titre : Spatial-temporal attentive LSTM for vehicle-trajectory prediction Type de document : Article/Communication Auteurs : Rui Jiang, Auteur ; Hongyun Xu, Auteur ; Gelian Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] données spatiotemporelles
[Termes IGN] navigation autonome
[Termes IGN] relation spatiale
[Termes IGN] système de transport intelligent
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurRésumé : (auteur) Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task because existing studies have mainly focused on the spatial interactions of adjacent vehicles regardless of the temporal dependencies. In this paper, we propose a spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) to predict vehicle trajectories. Specifically, the spatial attention mechanism is used to capture the spatial relationships among neighboring vehicles and then obtain the global spatial feature. Meanwhile, the temporal attention mechanism is designed to distinguish the effects of different historical time steps on future trajectory prediction. In addition, the motion feature of vehicles is extracted to reveal the influence of dynamic information on vehicle-trajectory prediction, and is combined with the local and global spatial features to represent the integrated features of the target vehicle at each historical moment. The experiments were conducted on public highway trajectory datasets—US-101 and I-80 in NGSIM—and the results demonstrate that our model achieves state-of-the-art prediction performance. Numéro de notice : A2022-549 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070354 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.3390/ijgi11070354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101150
in ISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 354[article]Street-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkTemporal transitions of demographic dot maps / Jeff Allen in International journal of cartography, vol 8 n° 2 (July 2022)PermalinkEstimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique / Syaza Rozali in Geocarto international, vol 37 n° 11 ([15/06/2022])PermalinkAnalysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkArtificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkBeyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkCharacteristics of disease maps of zoonoses: A scoping review and a recommendation for a reporting guideline for disease maps / Inthuja Selvaratnam in Cartographica, vol 57 n° 2 (Summer 2022)PermalinkContext-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkCoupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDetecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)Permalink