Descripteur
Documents disponibles dans cette catégorie (1332)
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
Exploration 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)
[article]
Titre : Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks Type de document : Article/Communication Auteurs : Enrico Steiger, Auteur ; Bernd Resch, Auteur ; Alexander Zipf, Auteur Année de publication : 2016 Article en page(s) : pp 1694 - 1716 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte de Kohonen
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données hétérogènes
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] traitement de données localisées
[Termes IGN] TwitterRésumé : (Auteur) The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data. Numéro de notice : A2016-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1144887 En ligne : http://dx.doi.org/10.1080/13658816.2016.1144887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81707
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1694 - 1716[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Modeling spatiotemporal information generation / Simon Scheider in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Modeling spatiotemporal information generation Type de document : Article/Communication Auteurs : Simon Scheider, Auteur ; Benedikt Gräler, Auteur ; Edzer J. Pebesma, Auteur ; Christophe Stasch, Auteur Année de publication : 2016 Article en page(s) : pp 1980 - 2008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données dérivée
[Termes IGN] données hétérogènes
[Termes IGN] exploration de données géographiques
[Termes IGN] information géographique
[Termes IGN] mise à jour de base de données
[Termes IGN] mise à jour en continu
[Termes IGN] regroupement de données
[Termes IGN] source de donnéesRésumé : (Auteur) Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of ‘data mining’ and ‘big data’ are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process of data gathering. A generative model of spatiotemporal information could not only help automating the description of derivation processes but also assessing the scope of a dataset’s future use by exploring possible transformations. Even though there are technical approaches to document data provenance, models for describing how spatiotemporal data are generated are still missing. To fill this gap, we introduce an algebra that models data generation and describes how datasets are derived, in terms of types of reference systems. We illustrate its versatility by applying it to a number of derivation scenarios, ranging from field aggregation to trajectory generation, and discuss its potential for retrieval, analysis support systems, as well as for assessing the space of meaningful computations. Numéro de notice : A2016-573 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1151520 En ligne : http://dx.doi.org/10.1080/13658816.2016.1151520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81729
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1980 - 2008[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Noise 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)
[article]
Titre : Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Qiangqiang Yuan, Auteur ; Huanfeng Shen, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 5425 - 5439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] bruit (théorie du signal)
[Termes IGN] données clairsemées
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] représentation parcimonieuseRésumé : (Auteur) Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral-spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral-spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods. Numéro de notice : A2016-902 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2564639 En ligne : https://doi.org/10.1109/TGRS.2016.2564639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83095
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5425 - 5439[article]Airborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)
[article]
Titre : Airborne lidar estimation of aboveground forest biomass in the absence of field inventory Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Sassan Saatchi, Auteur ; Clément Mallet , Auteur ; Stéphane Jacquemoud, Auteur ; Gil Rito-Gonçalves , Auteur ; Carlos Alberto Silva, Auteur ; Paola Soares, Auteur ; Margarida Tomé, Auteur ; Luisa Pereira, Auteur Année de publication : 2016 Projets : 3-projet - voir note / Article en page(s) : pp 1 - 18 Note générale : Bibliographie
This work was supported in part by the Portuguese Foundation for Science and Technology under Grant PTDC/AGR-CFL/72380/2006, co-financed by the European Fund of Regional Development (FEDER) through COMPETE—Operational Factors of Competitiveness Program (POFC) and the Grant Pest-OE/EEI/UI308/2014Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] allométrie
[Termes IGN] analyse de groupement
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] classification automatique d'objets
[Termes IGN] couvert végétal
[Termes IGN] dendrométrie
[Termes IGN] données lidar
[Termes IGN] extraction d'arbres
[Termes IGN] fiabilité des données
[Termes IGN] houppier
[Termes IGN] Portugal
[Termes IGN] puits de carbone
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree- or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO2 stocks and changes to comply with the UN-REDD policies. Numéro de notice : A2016--104 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs8080653 Date de publication en ligne : 12/08/2016 En ligne : https://doi.org/10.3390/rs8080653 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84675
in Remote sensing > vol 8 n° 8 (August 2016) . - pp 1 - 18[article]Documents numériques
en open access
A2016--104_Airborne_lidar_estimation_of_aboveground_forest_biomassAdobe Acrobat PDF An immune genetic algorithm to buildings displacement in cartographic generalization / Yageng Sun in Transactions in GIS, vol 20 n° 4 (August 2016)
[article]
Titre : An immune genetic algorithm to buildings displacement in cartographic generalization Type de document : Article/Communication Auteurs : Yageng Sun, Auteur ; Qingsheng Guo, Auteur ; Yuangang Liu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 585 - 612 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme génétique
[Termes IGN] bâtiment
[Termes IGN] contrainte géométrique
[Termes IGN] contrainte relationnelle
[Termes IGN] déplacement d'objet géographique
[Termes IGN] généralisation automatique de données
[Termes IGN] généralisation cartographique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Spatial conflicts may occur when map data are displayed at a scale smaller than that of the source map. This study applies the displacement operator in cartographic generalization to resolve such spatial conflicts and to improve the clarity and legibility of map. The immune genetic algorithm (IGA) is used in this study for buildings displacement to solve conflicts. IGA is based on the genetic algorithm (GA) and employs the self-adjusting mechanism of antibody concentration to enhance population diversity. Meanwhile, the elitism retention strategy is adopted in IGA to guarantee that the best individual (antibody) is not lost and destroyed in the next generation to strengthen convergence efficiency. The compared experiment between IGA and GA shows that the displacement result produced by IGA performs better than GA. Finally, in order to make the displaced map more attractive to cartographers, two constraints – the building alignment constraint and building tangent relation constraint – are applied in IGA to restrict the buildings’ displacement. The same experimental data are adopted to prove that the improved IGA is useful for maintaining the two constraints. Numéro de notice : A2016--053 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12165 En ligne : http://dx.doi.org/10.1111/tgis.12165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83771
in Transactions in GIS > vol 20 n° 4 (August 2016) . - pp 585 - 612[article]Dirichlet 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 - August 2016)PermalinkA hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkLearning-based superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkModeling of ionosphere time series using wavelet neural networks (case study: N-W of Iran) / Mir Reza Ghaffari Razin in Advances in space research, vol 58 n° 1 (July 2016)Permalink