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A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)
[article]
Titre : A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data Type de document : Article/Communication Auteurs : Biao He, Auteur ; Zhang Yan, Auteur ; Yu Chen, Auteur ; Zhihui Gu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bicyclette
[Termes IGN] entropie
[Termes IGN] extraction de modèle
[Termes IGN] origine - destination
[Termes IGN] raisonnement spatial
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. Numéro de notice : A2018-345 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7060203 Date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.10.3390/ijgi7060203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90568
in ISPRS International journal of geo-information > vol 7 n° 6 (June 2018)[article]The map as knowledge base / Dalia E. Varanka in International journal of cartography, vol 4 n° 2 (June 2018)
[article]
Titre : The map as knowledge base Type de document : Article/Communication Auteurs : Dalia E. Varanka, Auteur ; E. Lynn Usery, Auteur Année de publication : 2018 Article en page(s) : pp 201 - 223 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] base de connaissances
[Termes IGN] carte
[Termes IGN] interface utilisateur
[Termes IGN] modèle logique de données
[Termes IGN] PostgreSQL
[Termes IGN] SPARQL
[Termes IGN] visualisation cartographique
[Termes IGN] web sémantiqueRésumé : (Auteur) This paper examines the concept and implementation of a map as a knowledge base. A map as a knowledge base means that the visual map is not only the descriptive compilation of data and design principles, but also involves a compilation of semantic propositions and logical predicates that create a body of knowledge organized as a map. The digital product of a map as knowledge base can be interpreted by machines, as well as humans, and can provide access to the knowledge base through interfaces to select features and other information from the map. The design of maps as a knowledge base involves technical approaches and a system architecture to support a knowledge base. This paper clarifies how a map as a knowledge base differs from earlier map theory models by investigating the knowledge-based concepts of implementation through logical modelling, a knowledge repository, user interfaces for information access, and cartographic visualization. The paper ends with proof of concepts for two types of cartographic data query. Numéro de notice : A2018-426 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2017.1421004 Date de publication en ligne : 20/05/2018 En ligne : https://doi.org/10.1080/23729333.2017.1421004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90919
in International journal of cartography > vol 4 n° 2 (June 2018) . - pp 201 - 223[article]A voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)
[article]
Titre : A voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation Type de document : Article/Communication Auteurs : Yusheng Xu, Auteur ; Ludwig Hoegner, Auteur ; Sebastian Tuttas, Auteur ; Uwe Stilla, Auteur Année de publication : 2018 Article en page(s) : pp 377 - 391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bati
[Termes IGN] données localisées 3D
[Termes IGN] octree
[Termes IGN] partition des données
[Termes IGN] prise en compte du contexte
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] théorie des graphes
[Termes IGN] voxelRésumé : (auteur) In this paper, we report a novel strategy for segmenting 3D point clouds using a voxel structure and graph-based clustering with perceptual grouping laws. It provides a completely automatic solution for partitioning point clouds of man-made infrastructure. Two different segmentation methods using voxel and supervoxel structures are presented and evaluated. To increase the efficiency and the robustness of the segmentation process, the voxelization with octree-based structure is introduced, which can suppress effects of noise, outliers, and unevenly distributed point densities as well. The clustering of over-segmented voxels and supervoxels is achieved using graph theory on the basis of the local contextual information, which is commonly conducted merely with pairwise information in conventional clustering algorithms. The graphical model is constructed according to perceptual grouping laws, considering geometric information associated with points. Experiments using both laser scanning and photogrammetric point clouds have demonstrated that the proposed methods can achieve good results, especially complex scenes and nonplanar object surfaces, with F1-measures better than 0.67 for all the testing samples. Quantitative comparisons between the proposed approaches and other representative segmentation methods also confirm the effectiveness and the efficiency of the former. Moreover, a series of experiments is carried out, to investigate the methods' sensitivity with respect to various parameters on the segmentation results. Numéro de notice : A2018-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.6.377 Date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.14358/PERS.84.6.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90173
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 6 (juin 2018) . - pp 377 - 391[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2018061 RAB Revue Centre de documentation En réserve L003 Disponible An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)
[article]
Titre : An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery Type de document : Article/Communication Auteurs : Luis Angel Ruiz, Auteur ; Jorge Abel Recio, Auteur ; Pablo Crespo-Peremarch, Auteur ; Marta Sapena Moll, Auteur Année de publication : 2018 Article en page(s) : pp 443 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre de décision
[Termes IGN] biomasse (combustible)
[Termes IGN] carte forestière
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt méditerranéenne
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. Numéro de notice : A2018-140 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265595 Date de publication en ligne : 28/11/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89690
in Geocarto international > vol 33 n° 5 (May 2018) . - pp 443 - 457[article]Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network Type de document : Article/Communication Auteurs : Ruibin Zhao, Auteur ; Mingyong Pang, Auteur ; Jidong Wang, Auteur Année de publication : 2018 Article en page(s) : pp 960 - 979 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. Numéro de notice : A2018-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1431840 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1431840 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89861
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 960 - 979[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data / Kristin Stock in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkDeep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkDo semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)PermalinkA formalized 3D geovisualization illustrated to selectivity purpose of virtual 3D city model / Romain Neuville in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)PermalinkA geometric-based approach for road matching on multi-scale datasets using a genetic algorithm / Alireza Chehreghan in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkLarge-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkLocal curvature entropy-based 3D terrain representation using a comprehensive Quadtree / Giyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkBinary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkCrowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)PermalinkDésambiguïsation des entités spatiales par apprentissage actif / Amal Chihaoui in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)Permalink