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Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7351 - 7365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features. Numéro de notice : A2016-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2600522 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2600522 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83346
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7351 - 7365[article]Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing Type de document : Article/Communication Auteurs : Lei Zhang, Auteur ; Wei Wei, Auteur ; Yanning Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7223 - 7235 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bruit blanc
[Termes IGN] compression d'image
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models. With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved. Numéro de notice : A2016-929 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598577 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598577 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83343
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7223 - 7235[article]Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification Type de document : Article/Communication Auteurs : Zhenxin Zhang, Auteur ; Liqiang Zhang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7309 - 7322 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] classificateur
[Termes IGN] codage
[Termes IGN] extraction de points
[Termes IGN] problème de Dirichlet
[Termes IGN] semis de pointsRésumé : (Auteur) Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification. Numéro de notice : A2016-931 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2599163 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2599163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83345
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7309 - 7322[article]Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks / Yu Lei in Artificial satellites, vol 51 n° 4 (December 2016)
[article]
Titre : Ultra short-term prediction of pole coordinates via combination of empirical mode decomposition and neural networks Type de document : Article/Communication Auteurs : Yu Lei, Auteur ; Danning Zhao, Auteur ; Hongbing Cai, Auteur Année de publication : 2016 Article en page(s) : pp 149 – 161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] filtre passe-bas
[Termes IGN] fonction de base radiale
[Termes IGN] mouvement du pôle
[Termes IGN] oscillation
[Termes IGN] prévision à court terme
[Termes IGN] réseau neuronal artificiel
[Termes IGN] terme de ChandlerRésumé : (auteur) It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques. Numéro de notice : A2016-977 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/arsa-2016-0013 En ligne : https://doi.org/10.1515/arsa-2016-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83688
in Artificial satellites > vol 51 n° 4 (December 2016) . - pp 149 – 161[article]Usability of an opportunistic interface concept for ad hoc ride-sharing / Michael Rigby in International journal of cartography, vol 2 n° 2 (December 2016)
[article]
Titre : Usability of an opportunistic interface concept for ad hoc ride-sharing Type de document : Article/Communication Auteurs : Michael Rigby, Auteur ; Stephan Winter, Auteur Année de publication : 2016 Article en page(s) : pp 115 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] autopartage
[Termes IGN] convivialité
[Termes IGN] ingénierie des connaissances
[Termes IGN] interface web
[Termes IGN] recherche du chemin optimal, algorithme de
[Termes IGN] site web
[Termes IGN] système de transport intelligentRésumé : (Auteur) Interacting with ride-sharing systems for ad hoc travel is a complex spatio-temporal task. The dynamics of service supply and demand challenge the rigidity of traditional human–computer interfaces, introducing service uncertainty and creating a knowledge gap which hinders a client's travel planning. Such interface constraints may mean that a client user is unable to find any ride matching their intentions. To overcome this, a novel visual interface concept, launch pads, has been suggested to replace the traditional interface within a two-step negotiation. To close the proposed approach's feedback loop, this paper investigates human understanding and use of the launch pad metaphor. Usability testing of launch pads is performed using a spatial cognitive engineering approach in directed wayfinding scenarios using various alternative representations. Results highlight that the variances of user interaction times depend on the representation used and reveal potential information overload issues. Using these findings, a minimum decision-making time is defined to tune the system's architecture. Numéro de notice : A2016--060 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2016.1145040 En ligne : http://dx.doi.org/10.1080/23729333.2016.1145040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84215
in International journal of cartography > vol 2 n° 2 (December 2016) . - pp 115 - 147[article]Fast three-dimensional empirical mode decomposition of hyperspectral images for class-oriented multitask learning / Zhi He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkHow many samples are needed? An investigation of binary logistic regression for selective omission in a road network / Qi Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)PermalinkMultiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkRobust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkSemi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkThe socio-environmental data explorer (SEDE) : a social media–enhanced decision support system to explore risk perception to hazard events / Eric Shook in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkKnowledge transfer for large-scale urban growth modeling based on formal concept analysis / Jinyao Lin in Transactions in GIS, vol 20 n° 5 (October 2016)PermalinkMining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkA new ZTD model based on permanent ground-based GNSS-ZTD data / M. Ding in Survey review, vol 48 n° 351 (October 2016)PermalinkOn discovering co-location patterns in datasets : a case study of pollutants and child cancers / Jundong Li in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkSAR image change detection based on correlation kernel and multistage extreme learning machine / Lu Jia in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkSPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks / Julian Hagenauer in Transactions in GIS, vol 20 n° 5 (October 2016)PermalinkÉvaluation de la qualité des sources du Web de Données pour la résolution d'entités nommées / Carmen Brando in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 5 - 6 (septembre - décembre 2016)PermalinkExploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks / Enrico Steiger in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkModeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)PermalinkAn immune genetic algorithm to buildings displacement in cartographic generalization / Yageng Sun in Transactions in GIS, vol 20 n° 4 (August 2016)PermalinkDirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkDisaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkSea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study / Lei Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkUnsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)PermalinkAutomatic delineation of built-up area at urban block level from topographic maps / Sebastian Muhs in Computers, Environment and Urban Systems, vol 58 (July 2016)PermalinkEfficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkEnabling maps/location searches on mobile devices: constructing a POI database via focused crawling and information extraction / Hsiu-Min Chuang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - 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août 2016)PermalinkAn assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)PermalinkAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkA multilevel point-cluster-based discriminative feature for ALS point cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkSpatial discovery and the research library / Sara Lafia in Transactions in GIS, vol 20 n° 3 (June 2016)PermalinkAnalysis of human mobility patterns from GPS trajectories and contextual information / Katarzyna Siła-Nowicka in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkATLAS: A three-layered approach to facade parsing / Markus Mathias in International journal of computer vision, vol 118 n° 1 (May 2016)PermalinkAutonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)PermalinkDeep filter banks for texture recognition, description, and segmentation / Mircea Cimpoi in International journal of computer vision, vol 118 n° 1 (May 2016)PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkKernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkLearning grammars for architecture-specific facade parsing / Raghudeep Gadde in International journal of computer vision, vol 117 n° 3 (May 2016)PermalinkA new method for discovering behavior patterns among animal movements / Yuwei Wang in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkOnline interactive thematic mapping: Applications and techniques for socio-economic research / Duncan A. Smith in Computers, Environment and Urban Systems, vol 57 (May 2016)PermalinkReconstruction of itineraries from annotated text with an informed spanning tree algorithm / Ludovic Moncla in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkSimulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China / Fengmei Yao in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkActive-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkExploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) / Ran Wang in Geoinformatica, vol 20 n° 2 (April - June 2016)PermalinkIntegrating geo web services for a user driven exploratory analysis / Simon Moncrieff in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkOptimisation d'un service d'autopartage de véhicules électriques / Amine Ait-Ouahmed in Revue internationale de géomatique, vol 26 n° 2 (avril - juin 2016)PermalinkProject pointless : pathfinding through identified empty space in point clouds / Tom Broersen in GIM international [en ligne], vol 30 n° 4 (April 2016)PermalinkSimuler les mobilités individuelles : Les enjeux de l'information géographique / Jean-Philippe Antoni in Revue internationale de géomatique, vol 26 n° 2 (avril - juin 2016)PermalinkStreet-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkClassified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkModelling the spatial evolution of map objects by map agents / Shen Ying in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkMulti-agent based path planning for first responders among moving obstacles / Zhiyong Wang in Computers, Environment and Urban Systems, vol 56 (March 2016)PermalinkOctree-based segmentation for terrestrial LiDAR point cloud data in industrial applications / Yun-Ting Su in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkSLEUTH* : un modèle d’expansion urbaine scénario-dépendant / Omar Doukari in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)PermalinkFeature-driven generalization of isobaths on nautical charts: A multi-agent system approach / Eric Guilbert in Transactions in GIS, vol 20 n° 1 (February 2016)PermalinkImage based geo-localization in the Alps / Olivier Saurer in International journal of computer vision, vol 116 n° 3 (February 2016)PermalinkObject classification and recognition from mobile laser scanning point clouds in a road environment / Matti Lehtomäki in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkSpace–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkA joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)PermalinkPermalinkPermalinkAutomatisation de la généralisation cartographique : Relations et interactions, orchestration et approches multi-agents / Cécile Duchêne (2016)PermalinkAutonomous navigation in complex nonplanar environments based on laser ranging / Philipp Andreas Krüsi (2016)PermalinkBig data, open data et valorisation des données / Jean-Louis Monino (2016)PermalinkBIM and cultural heritage: compatibility tests in an archaeological site / Nora Lombardini ; Cinzia Tommasi in International journal of 3-D information modeling, vol 5 n° 1 (January - March 2016)PermalinkPermalinkDense image matching / Martin Kodde in GIM international [en ligne], vol 30 n° 1 (January 2016)PermalinkEducational values and services of ecosystems and landscapes : An overview / Ewelina Mocior in Ecological indicators, vol 60 (January 2016)PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkExploring mass variations in the Earth system / Mike Sips in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkForêts aléatoires pour la détection des feux tricolores à partir de profils de vitesse GPS / Yann Méneroux (2016)PermalinkLe Grand Paris écologique, endroit très beau : reconnaissance des noms de lieu dans des corpus thématiques français, présenté lors de l'atelier EXtraction de Connaissances à partir de donnEes Spatialisées de SAGEO 2016 / Carmen Brando (2016)PermalinkIndoor navigation of mobile robots based on visual memory and image-based visual servoing / Suman Raj Bista (2016)PermalinkPermalinkPermalinkPersonal mobility pattern mining and anomaly detection in the GPS era / Dong-He Shih in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)PermalinkPhotogrammetric computer vision / Wolfgang Förstner (2016)PermalinkPermalinkQGIS 2 cookbook / Alex Mandel (2016)PermalinkRéduction du nombre des prédicats pour les approches de répartition des entrepôts de données / Mourad Ghorbel in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 1 (janvier - février 2016)PermalinkRevisiting cartography : towards identifying and developing a modern and comprehensive framework / Melih Basaraner in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkPermalinkPermalinkPermalinkPermalinkTowards process validation for complex transport models: A sensitivity analysis of a social network-enhanced activity-travel model / Nicole Ronald in Computers, Environment and Urban Systems, vol 55 (January 2016)PermalinkAn exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index / Jamal Jokar Arsanjani in Transactions in GIS, vol 19 n° 6 (December 2015)Permalink