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classification barycentriqueSynonyme(s)classification sur la distance minimale ;classification du k-proche voisin ;classification par minimum de distance classification par k centroïdesVoir aussi |
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Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)
Titre : Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois Type de document : Article/Communication Auteurs : Margarita Khokhlova , Auteur ; Valérie Gouet-Brunet , Auteur ; Nathalie Abadie , Auteur ; Liming Chen, Auteur Editeur : Vannes : Université de Bretagne Sud Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : RFIAP 2020, Reconnaissance des Formes, Image, Apprentissage et Perception 23/06/2020 26/06/2020 Vannes France Open Access Proceedings Importance : 11 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse du paysage
[Termes IGN] appariement d'images
[Termes IGN] architecture de réseau
[Termes IGN] BD ortho
[Termes IGN] BD Topo
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] données multitemporelles
[Termes IGN] géolocalisation
[Termes IGN] image aérienne
[Termes IGN] image multitemporelle
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] réseau neuronal siamois
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Cet article présente un réseau multimodal qui met en correspondance des images aériennes de territoires urbains et ruraux français prises à environ 15 ans d'intervalle. Il devrait être invariant à un large éventail de changements, tels que l'évolution du paysage au fil des années. Il exploite les images originales et les régions sémantiquement segmentées et étiquetées. Le coeur de la méthode est un réseau siamois qui apprend à extraire des caractéristiques des paires d'images correspondantes dans le temps et des paires non correspondantes. Ces descripteurs sont suffisamment discriminants pour qu'un simple classifieur k-NN suffise comme critère de géo-correspondance final. Dans cet article, nous dé-montrons que notre descripteur siamois surpasse les autres descripteurs d'images en termes de recherche d'images par contenu à travers le temps. Numéro de notice : C2020-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésNat DOI : sans En ligne : https://cap-rfiap2020.sciencesconf.org/data/RFIAP_2020_paper_21.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95446 Voir aussiDocuments numériques
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rfiap2020_21_cameraready.pdfAdobe Acrobat PDF
Titre : Spatial big data, BIM and advanced GIS for smart transformation Type de document : Monographie Auteurs : Sara Shirowzhan, Éditeur scientifique ; Willie Tan, Éditeur scientifique ; Samad R.E. Sepasgozar, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 166 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-031-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification barycentrique
[Termes IGN] cycliste
[Termes IGN] données massives
[Termes IGN] modèle orienté agent
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] optimisation (mathématiques)
[Termes IGN] planification urbaine
[Termes IGN] réseau ferroviaire
[Termes IGN] réseau routier
[Termes IGN] secours d'urgence
[Termes IGN] système d'information géographique
[Termes IGN] téléphone intelligent
[Termes IGN] trafic routier
[Termes IGN] ville intelligenteRésumé : (éditeur) This book covers a range of topics including selective technologies and algorithms that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This book also presents an agenda for future investigations to address the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities. Some of selected specific tools presented in this book are as a simulator for improving the smart parking practices by modelling drivers with activity plans, a bike optimization algorithm to increase the efficiency of bike stations, an agent-based model simulation of human mobility with the use of mobile phone datasets. In addition, this book describes the use of numerical methods to match the network demand and supply of bicycles, investigate the distribution of railways using different indicators, presents a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks, and presents an efficient staged evacuation planning algorithm for multi-exit buildings. Note de contenu : 1- Digital twin and cyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities
2- An efficient staged evacuation planning algorithm applied to multi-exit buildings
3- A hybrid framework for high-performance modeling of three-dimensional pipe networks
4- Direction-aware continuous moving K-nearest-neighbor query in road networks
5- The distribution pattern of the railway network in China at the county level
6- Data-driven bicycle network analysis based on traditional counting methods and GPS traces from smartphone
7- An agent-based model simulation of human mobility based on mobile phone data: How commuting relates to congestion
8- Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in Shenzhen, China
9- An occupancy simulator for a smart parking system: Developmental design and experimental considerationsNuméro de notice : 28440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-031-4 En ligne : https://doi.org/10.3390/books978-3-03936-031-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98877 Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])
[article]
Titre : Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco Type de document : Article/Communication Auteurs : Ali Aydda, Auteur ; Omar F. Althuwaynee, Auteur ; Ahmed Algouti, Auteur ; Abdellah Algouti, Auteur Année de publication : 2019 Article en page(s) : pp 1514 - 1529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte géomorphologique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dune
[Termes IGN] image Landsat
[Termes IGN] image multitemporelle
[Termes IGN] littoral
[Termes IGN] Maroc
[Termes IGN] sable
[Termes IGN] vent de sableRésumé : (auteur) The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats. Numéro de notice : A2019-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1493154 Date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1493154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93820
in Geocarto international > vol 34 n° 13 [15/10/2019] . - pp 1514 - 1529[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019131 RAB Revue Centre de documentation En réserve L003 Disponible Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa-Pillai-Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)
[article]
Titre : Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa-Pillai-Vijayakumar , Auteur ; Jean-Pierre Renaud , Auteur ; François Morneau , Auteur ; Ronald E. McRoberts, Auteur ; Cédric Vega , Auteur Année de publication : 2019 Projets : DIABOLO / Packalen, Tuula Article en page(s) : n° 991 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre caducifolié
[Termes IGN] classification barycentrique
[Termes IGN] feuillu
[Termes IGN] image Landsat-8
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] Orléans, forêt domaniale d' (Loiret)
[Termes IGN] photogrammétrie numérique
[Termes IGN] Pinus pinaster
[Termes IGN] Pinus sylvestris
[Termes IGN] Quercus pedunculata
[Termes IGN] Quercus sessiliflora
[Termes IGN] Sologne (France)
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Numéro de notice : A2019-382 Affiliation des auteurs : LIF+Ext (2012-2019) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11080991 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.3390/rs11080991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93456
in Remote sensing > vol 11 n° 8 (August 2019) . - n° 991[article]Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)
[article]
Titre : Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data Type de document : Article/Communication Auteurs : Piotr Tompalski, Auteur ; Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 110 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode robuste
[Termes IGN] modèle mathématique
[Termes IGN] régression
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of Numéro de notice : A2019-227 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.04.006 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1016/j.rse.2019.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92741
in Remote sensing of environment > vol 227 (15 June 2019) . - pp 110 - 124[article]Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 ([15/03/2019])PermalinkA simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])PermalinkBridging the gap: toward a French MS-NFI for territories / Jean-Pierre Renaud (2019)PermalinkSpatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data / Nikolaos Sideris (2019)PermalinkStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPredicting tree diameter distributions from airborne laser scanning, SPOT 5 satellite, and field sample data in the perm region, Russia / Jussi Peuhkurinen in Forests, vol 9 n° 10 (October 2018)PermalinkIncorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning / Matti Maltamo in Silva fennica, vol 52 n° 3 ([01/08/2018])PermalinkSpatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)PermalinkAn 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)Permalink