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modèle dynamiqueSynonyme(s)modèle spatiotemporel dynamique |
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Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica, vol 24 n° 3 (July 2020)
[article]
Titre : Learning evolving user’s behaviors on location-based social networks Type de document : Article/Communication Auteurs : Ruizhi Wu, Auteur ; Guangchun Luo, Auteur ; Qi jin, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 713 – 743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] comportement
[Termes IGN] données localisées des bénévoles
[Termes IGN] filtrage d'information
[Termes IGN] géopositionnement
[Termes IGN] interaction homme-milieu
[Termes IGN] modèle dynamique
[Termes IGN] réseau social géodépendant
[Termes IGN] utilisateurRésumé : (auteur) With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper, we propose a new dynamic model that considers both geo-aware user preferences and the social interaction excitation arising from social connections to learn the dynamic mobility mechanism of user’s behaviors on LBSNs. Geo-aware location features, such as semantic features, latent features and dynamic features, are utilized to characterize the location information and reveal the evolution of the geographical impact of location. These geo-aware location features enable us to exploit user’s personal preferences. Meanwhile, we integrate a user’s social connections and friends’ preferences for modeling social interaction excitations. Finally, we jointly incorporate geo-aware user preference learning and social interaction excitation modeling to create a conditional intensity function for temporal point processes with which to explore the dynamic mobility mechanism of evolving user’s check-in behaviors on LBSNs. Extensive experiments on several real-world check-in datasets confirm that our proposed algorithm performs better than existing state-of-the-art methods. Numéro de notice : A2020-372 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00400-3 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1007/s10707-020-00400-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95267
in Geoinformatica > vol 24 n° 3 (July 2020) . - pp 713 – 743[article]Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests / Jiaxin Chen in Forest ecology and management, Vol 466 (15 June 2020)
[article]
Titre : Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests Type de document : Article/Communication Auteurs : Jiaxin Chen, Auteur ; Hongqiang Yang, Auteur ; Rongzhou Man, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Canada
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données environnementales
[Termes IGN] données spatiotemporelles
[Termes IGN] forêt boréale
[Termes IGN] gestion forestière durable
[Termes IGN] hauteur des arbres
[Termes IGN] modèle dynamique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surveillance forestièreRésumé : (auteur) Sustainable forest management requires the ability to accurately model forest dynamics under a changing environment, which is difficult using conventional statistical methods as many factors that interactively affect forest growth must be considered. As well, statistical model development is often limited by the lack of broad-scale repeated forest measurements needed to capture changes in 1 or more variables and the corresponding changes in forest dynamics (e.g., growth in diameter and height), while assuming other variables do not change, or their changes do not significantly affect the forest dynamics of interest. In many forested countries, comprehensive monitoring programs have amassed large amounts of diverse forest measurement data. Here we propose a new approach for using artificial neural network-based machine learning to synthesize spatiotemporal tree measurement data collected over a vast area of boreal forest in central Canada to model diameter at breast height (DBH)-height and DBH-height-age relationships for 6 dominant tree species. More than 30 potentially important stand structure, site, and climate variables were considered. We used an individual-based modelling approach by considering each individual tree measurement as an instance of the complex relationships modelled; together, broad-scale long-term monitoring data contain many such instances, representing considerable spatial and temporal scale variation in forest growth and growing conditions. Using this approach, we significantly improved DBH-height and DBH-height-age models. And the models developed allowed us to analyze the effects of environmental conditions or changes in these conditions on forest growth. This may be the first attempt at applying this type of approach, which can be used to more accurately model, for example, forest growth, mortality, and how they are affected by changing climate in a variety of forest types. Numéro de notice : A2020-406 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118104 Date de publication en ligne : 04/04/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118104 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95463
in Forest ecology and management > Vol 466 (15 June 2020)[article]Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
[article]
Titre : Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method Type de document : Article/Communication Auteurs : Zhenzhong Peng, Auteur ; Ru Wang, Auteur ; Lingbo Liu, Auteur ; Hao Wu, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] bati
[Termes IGN] densité de population
[Termes IGN] diagramme de Voronoï
[Termes IGN] distribution spatiale
[Termes IGN] données démographiques
[Termes IGN] espace urbain
[Termes IGN] modèle de régression
[Termes IGN] modèle dynamique
[Termes IGN] petite échelle
[Termes IGN] régression géographiquement pondérée
[Termes IGN] téléphone intelligentRésumé : (auteur) Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data. Numéro de notice : A2020-315 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060344 Date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060344 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95170
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 16 p.[article]Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
[article]
Titre : Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation Type de document : Article/Communication Auteurs : Eslam Ali, Auteur ; Wenbin Xu, Auteur ; Xiao-Li Ding, Auteur Année de publication : 2020 Article en page(s) : pp 106 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] correction des ombres
[Termes IGN] COSI-Corr
[Termes IGN] déplacement d'objet géographique
[Termes IGN] désert
[Termes IGN] désertification
[Termes IGN] données météorologiques
[Termes IGN] dune
[Termes IGN] image Landsat-8
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] incertitude des données
[Termes IGN] modèle d'inversion
[Termes IGN] modèle dynamique
[Termes IGN] prévention des risques
[Termes IGN] sable
[Termes IGN] série temporelle
[Termes IGN] Sinai
[Termes IGN] variation saisonnière
[Termes IGN] vent de sableRésumé : (auteur) Sand dune migration poses a potential threat to desert infrastructure, vegetation, and atmospheric conditions. Capturing the patterns of long-term dune migration is useful for predicting probable desertification issues and wind conditions across vast desert areas. In this study, we employed optical image matching and a singular value decomposition approach to estimate the rates of dune migration in the North Sinai Sand Sea using the free Landsat 8 and Sentinel-2 archives. Our optical image matching time-series selection and inversion (OPTSI) algorithm limited the difference in the solar illumination of correlated pairs to decrease shadows and seasonal variability. We found that the maximum annual dune migration rates were 9.4 m/a and 15.9 m/a for Landsat 8 and Sentinel-2 data, respectively, and the results of time-series analysis revealed the existence of seasonal variations in dune migration controlled by wind regimes. The directions of sand movement extracted from the mean velocity solution agreed strongly with each other and with the drift directions estimated using wind data from meteorological stations. We assessed the uncertainty of each solution based on the variance of stable areas. Our results showed that the proposed inversion decreased uncertainty by up to 25% and increased the spatial coverage by up to 20%. This algorithm is also promising for the retrieval of historical time series on the ground displacements of glaciers and slow-moving landslides employing free archives that provide high-frequency images. Numéro de notice : A2020-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.004 Date de publication en ligne : 27/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94997
in ISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 106 - 124[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Visualizing when, where, and how fires happen in U.S. parks and protected areas / Nicole C. Inglis in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Visualizing when, where, and how fires happen in U.S. parks and protected areas Type de document : Article/Communication Auteurs : Nicole C. Inglis, Auteur ; Jelena Vukomanovic, Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aire protégée
[Termes IGN] changement climatique
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] géodatabase
[Termes IGN] incendie de forêt
[Termes IGN] lutte contre l'incendie
[Termes IGN] modèle dynamique
[Termes IGN] parc naturel national
[Termes IGN] prévention des risques
[Termes IGN] réserve naturelle
[Termes IGN] variation saisonnière
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Fire management in protected areas faces mounting obstacles as climate change alters disturbance regimes, resources are diverted to fighting wildfires, and more people live along the boundaries of parks. Evidence-based prescribed fire management and improved communication with stakeholders is vital to reducing fire risk while maintaining public trust. Numerous national fire databases document when and where natural, prescribed, and human-caused fires have occurred on public lands in the United States. However, these databases are incongruous and non-standardized, making it difficult to visualize spatiotemporal patterns of fire and engage stakeholders in decision-making. We created interactive decision analytics (“VISTAFiRe”) that transform fire history data into clear visualizations of the spatial and temporal dimensions of fire and its management. We demonstrate the utility of our approach using Big Cypress National Preserve and Everglades National Park as examples of protected areas experiencing fire regime change between 1980 and 2017. Our open source visualizations may be applied to any data from the National Park Service Wildland Fire Events Geodatabase, with flexibility to communicate shifts in fire regimes over time, such as the type of ignition, duration and magnitude, and changes in seasonal occurrence. Application of the tool to Everglades and Big Cypress revealed that natural wildfires are occurring earlier in the wildfire season, while human-caused and prescribed wildfires are becoming less and more common, respectively. These new avenues of stakeholder communication are allowing the National Park Service to devise research plans to prepare for environmental change, guide resource allocation, and support decision-making in a clear and timely manner. Numéro de notice : A2020-298 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050333 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.3390/ijgi9050333 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95138
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 14 p.[article]A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)PermalinkSpatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkIntegration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkSpectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkLand use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process / Biswajit Nath in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)PermalinkObject‐oriented tracking of thematic and spatial behaviors of urban heat islands / Rui Zhu in Transactions in GIS, Vol 24 n° 1 (February 2020)PermalinkUn modèle spatio-temporel hybride de SIG temporel : application à la géomorphologie marine / Younes Hamdani (2020)PermalinkPermalinkSpatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)PermalinkPermalink