Descripteur
Termes IGN > aménagement > infrastructure > réseau technique > réseau de transport
réseau de transportSynonyme(s)réseau de communicationVoir aussi |
Documents disponibles dans cette catégorie (810)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Railway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Railway lidar semantic segmentation with axially symmetrical convolutional learning Type de document : Article/Communication Auteurs : Antoine Manier, Auteur ; Julien Moras, Auteur ; Jean-Christophe Michelin , Auteur ; Hélène Piet-Lahanier, Auteur Année de publication : 2022 Article en page(s) : pp 135 - 142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] scène 3D
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] voie ferréeRésumé : (auteur) This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy. Numéro de notice : A2022-433 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-135-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-135-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100739
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 135 - 142[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]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)
[article]
Titre : Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads Type de document : Article/Communication Auteurs : Raul de Paula Pires, Auteur ; Kenneth Olofsson, Auteur ; Henrik J. Persson, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 211 - 224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] collecte de données
[Termes IGN] détection d'arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] lidar mobile
[Termes IGN] route
[Termes IGN] semis de points
[Termes IGN] Suède
[Termes IGN] tronc
[Termes IGN] volume en boisRésumé : (Auteur) The collection of field-reference data is a key task in remote sensing-based forest inventories. However, traditional methods of collection demand extensive personnel resources. Thus, field-reference data collection would benefit from more automated methods. In this study, we proposed a method for individual tree detection (ITD) and stem attribute estimation based on a car-mounted mobile laser scanner (MLS) operating along forest roads. We assessed its performance in six ranges with increasing mean distance from the roadside. We used a Riegl VUX-1LR sensor operating with high repetition rate, thus providing detailed cross sections of the stems. The algorithm we propose was designed for this sensor configuration, identifying the cross sections (or arcs) in the point cloud and aggregating those into single trees. Furthermore, we estimated diameter at breast height (DBH), stem profiles, and stem volume for each detected tree. The accuracy of ITD, DBH, and stem volume estimates varied with the trees’ distance from the road. In general, the proximity to the sensor of branches 0–10 m from the road caused commission errors in ITD and over estimation of stem attributes in this zone. At 50–60 m from roadside, stems were often occluded by branches, causing omissions and underestimation of stem attributes in this area. ITD’s precision and sensitivity varied from 82.8% to 100% and 62.7% to 96.7%, respectively. The RMSE of DBH estimates ranged from 1.81 cm (6.38%) to 4.84 cm (16.9%). Stem volume estimates had RMSEs ranging from 0.0800 m3 (10.1%) to 0.190 m3 (25.7%), depending on the distance to the sensor. The average proportion of detected reference volume was highly affected by the performance of ITD in the different zones. This proportion was highest from 0 to 10 m (113%), a zone that concentrated most ITD commission errors, and lowest from 50 to 60 m (66.6%), mostly due to the omission errors in this area. In the other zones, the RMSE ranged from 87.5% to 98.5%. These accuracies are in line with those obtained by other state-of-the-art MLS and terrestrial laser scanner (TLS) methods. The car-mounted MLS system used has the potential to collect data efficiently in large-scale inventories, being able to scan approximately 80 ha of forests per day depending on the survey setup. This data collection method could be used to increase the amount of field-reference data available in remote sensing-based forest inventories, improve models for area-based estimations, and support precision forestry development. Numéro de notice : A2022-229 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.004 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100215
in ISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 211 - 224[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022051 SL Revue Centre de documentation Revues en salle Disponible 081-2022053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt An exact statistical method for analyzing co-location on a street network and its computational implementation / Wataru Morioka in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : An exact statistical method for analyzing co-location on a street network and its computational implementation Type de document : Article/Communication Auteurs : Wataru Morioka, Auteur ; Mei-Po Kwan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 798 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] co-positionnement
[Termes IGN] distance euclidienne
[Termes IGN] fonction K de Ripley
[Termes IGN] implémentation (informatique)
[Termes IGN] méthode statistique
[Termes IGN] réseau routier
[Termes IGN] Tokyo (Japon)
[Termes IGN] zone tamponRésumé : (auteur) In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. Numéro de notice : A2022-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1976409 Date de publication en ligne : 16/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1976409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100230
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 773 - 798[article]A 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)
[article]
Titre : A graph attention network for road marking classification from mobile LiDAR point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Tongtong Sun, Auteur ; Shuang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] noeud
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] semis de points
[Termes IGN] signalisation routièreRésumé : (auteur) The category of road marking is a crucial element in Mobile laser scanning systems’ (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and generalization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines. Numéro de notice : A2022-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.jag.2022.102735 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100124
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102735[article]Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkGIS-based employment availabilities by mode of transport in Kuwait / S. Alkheder in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkIdentification de relations spatiales par apprentissage profond sur des graphes / Azelle Courtial in Cartes & Géomatique, n° 247-248 (mars-juin 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)PermalinkRoad network generalization method constrained by residential areas / Zheng Lyu in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkDiscovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkSNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows / Qiliang Liu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkAttributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)PermalinkPermalinkContribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)PermalinkPhotogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkRepresenting vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)PermalinkRoad traffic crashes and emergency response optimization: a geo-spatial analysis using closest facility and location-allocation methods / Sulaiman Yunus in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkRobust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)PermalinkPermalinkSpécification et qualité du réseau cyclable, application à la recherche d’itinéraires / Raphaël Bres (2022)PermalinkUrban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkPermalinkPermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)Permalink