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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données spatiotemporelles
[Termes IGN] géopositionnement
[Termes IGN] graphe
[Termes IGN] modèle de simulation
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] service fondé sur la position
[Termes IGN] utilisateur
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660[article]Geovisualization of COVID-19: State of the art and opportunities / Yu Lan in Cartographica, vol 56 n° 1 (Spring 2021)
[article]
Titre : Geovisualization of COVID-19: State of the art and opportunities Type de document : Article/Communication Auteurs : Yu Lan, Auteur ; Michael R. Desjardins, Auteur ; Alexander Hohl, Auteur ; Eric Delmelle, Auteur Année de publication : 2021 Article en page(s) : pp 2 - 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] carte interactive
[Termes IGN] carte thématique
[Termes IGN] cube espace-temps
[Termes IGN] données spatiotemporelles
[Termes IGN] Etats-Unis
[Termes IGN] maladie virale
[Termes IGN] modèle dynamique
[Termes IGN] variation saisonnière
[Termes IGN] WebSIG
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Mapping the prevalence and spread of infectious diseases has never been more critical than during the COVID-19 pandemic. A plethora of Web-based GIS dashboards have been created that incorporate basic GIS functionality; these dashboards have served as platforms for rapid data sharing and real-time information, ultimately facilitating decision making. However, many of them have merely focused on presenting and monitoring cumulative or daily incidence of COVID-19 data, disregarding the temporal dimension. In this paper, we review the usefulness of GIS-based dashboards for mapping the prevalence of COVID-19, but also missed opportunities to emphasize the temporal component of the disease (cyclicity, seasonality). We suggest that advanced geovisualization techniques can be used to integrate the temporal component in interactive animated maps illustrating (a) the daily relative risk and the number of days a geographic region has been in a disease cluster, (b) the ratio between the observed and expected number of cases over time, and (c) mortality count dynamics in a space–time cube. We illustrate these approaches by using COVID-19 cases and death counts across the U.S. at the county level from 25 January 2020 to 1 October 2020. We discuss how each of these visualization approaches can promote the understanding of important public health concepts applied to the pandemic such as risk, spread, and mortality. Finally, we suggest future avenues to promote research at the intersection of space–time visualization and infectious diseases. Numéro de notice : A2021-409 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2020-0027 Date de publication en ligne : 15/03/2021 En ligne : https://doi.org/10.3138/cart-2020-0027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97730
in Cartographica > vol 56 n° 1 (Spring 2021) . - pp 2 - 13[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2021011 SL Revue Centre de documentation Revues en salle Disponible Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; Tahereh Bahraini, Auteur ; Hadi Sadoghi Yazdi, Auteur Année de publication : 2021 Article en page(s) : pp 3266 - 3276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification bayesienne
[Termes IGN] classification floue
[Termes IGN] distribution de Gauss
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] filtre de Gauss
[Termes IGN] image hyperspectrale
[Termes IGN] Matlab
[Termes IGN] processeur graphique
[Termes IGN] qualité des données
[Termes IGN] variableRésumé : (auteur) The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods. Numéro de notice : A2021-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939512 Date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.1109/TGRS.2019.2939512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97396
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3266 - 3276[article]Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)
[article]
Titre : Utilizing urban geospatial data to understand heritage attractiveness in Amsterdam Type de document : Article/Communication Auteurs : Sevim Sezi Karayazi, Auteur ; Gamze Dane, Auteur ; Bauke de Vries, Auteur Année de publication : 2021 Article en page(s) : n° 198 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Amsterdam (Pays-Bas)
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatiale
[Termes IGN] attractivité (aménagement)
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] gestion durable
[Termes IGN] image Flickr
[Termes IGN] musée
[Termes IGN] patrimoine
[Termes IGN] point d'intérêt
[Termes IGN] régression géographiquement pondérée
[Termes IGN] tourismeRésumé : (auteur) Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. Numéro de notice : A2021-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040198 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.3390/ijgi10040198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97424
in ISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 198[article]Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)
[article]
Titre : Recognition of varying size scene images using semantic analysis of deep activation maps Type de document : Article/Communication Auteurs : Shikha Gupta, Auteur ; A.D. Dileep, Auteur ; Veena Thenkanidiyoor, Auteur Année de publication : 2021 Article en page(s) : n° 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] compréhension de l'image
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] modèle conceptuel de données
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Understanding the complex semantic structure of scene images requires mapping the image from pixel space to high-level semantic space. In semantic space, a scene image is represented by the posterior probabilities of concepts (e.g., ‘car,’ ‘chair,’ ‘window,’ etc.) present in it and such representation is known as semantic multinomial (SMN) representation. SMN generation requires a concept annotated dataset for concept modeling which is infeasible to generate manually due to the large size of databases. To tackle this issue, we propose a novel approach of building the concept model via pseudo-concepts. Pseudo-concept acts as a proxy for the actual concept and gives the cue for its presence instead of actual identity. We propose to use filter responses from deeper convolutional layers of convolutional neural networks (CNNs) as pseudo-concepts, as filters in deeper convolutional layers are trained for different semantic concepts. Most of the prior work considers fixed-size (≈227×227) images for semantic analysis which suppresses many concepts present in the images. In this work, we preserve the true-concept structure in images by passing in their original resolution to convolutional layers of CNNs. We further propose to prune the non-prominent pseudo-concepts, group the similar one using kernel clustering and later model them using a dynamic-based support vector machine. We demonstrate that resulting SMN representation indeed captures the semantic concepts better and results in state-of-the-art classification accuracy on varying size scene image datasets such as MIT67 and SUN397. Numéro de notice : A2021-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01168-8 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00138-021-01168-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97898
in Machine Vision and Applications > vol 32 n° 2 (March 2021) . - n° 52[article]Simple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA / Milan Lazecky in Procedia Computer Science, vol 181 (2021)PermalinkA heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkIdentifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkTopoclimatic zoning of continental Chile / Donna Cortez in Journal of maps, vol 17 n° 2 (February 2021)PermalinkPermalinkPermalinkClustering et apprentissage profond sous contraintes pour l’analyse de séries temporelles : Application à l’analyse temporelle incrémentale en télédétection / Baptiste Lafabregue (2021)PermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkPermalinkDevelopment and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)Permalink