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Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets / Ming Li in Geoinformatica, vol 22 n° 3 (July 2018)  

Public [article]  inGeoinformatica > vol 22 n° 3 (July 2018) . - pp 541 - 561 Titre : Assessing spatiotemporal predictability of LBSN : a case study of three Foursquare datasets Type de document : Article/Communication Auteurs : Ming Li, Auteur ; Rene Westerholt, Auteur ; Hongchao Fan, Auteur ; Alexander Zipf, Auteur Année de publication : 2018 Article en page(s) : pp 541 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] comportement [Termes IGN] jeu de données localisées [Termes IGN] modèle de simulation [Termes IGN] prévision [Termes IGN] réseau social géodépendant [Termes IGN] ville Résumé : (Auteur) Location-based social networks (LBSN) have provided new possibilities for researchers to gain knowledge about human spatiotemporal behavior, and to make predictions about how people might behave through space and time in the future. An important requirement of successfully utilizing LBSN in these regards is a thorough understanding of the respective datasets, including their inherent potential as well as their limitations. Specifically, when it comes to predictions, we must know what we can actually expect from the data, and how we could maximize their usefulness. Yet, this knowledge is still largely lacking from the literature. Hence, this work explores one particular aspect which is the theoretical predictability of LBSN datasets. The uncovered predictability is represented with an interval. The lower bound of the interval corresponds to the amount of regular behaviors that can easily be anticipated, and represents the correct predication rate that any algorithm should be able to achieve. The upper bound corresponds to the amount of information that is contained in the dataset, and represents the maximum correct prediction rate that cannot be exceeded by any algorithms. Three Foursquare datasets from three American cities are studied as an example. It is found that, within our investigated datasets, the lower bound of predictability of the human spatiotemporal behavior is 27%, and the upper bound is 92%. Hence, the inherent potentials of the dataset for predicting human spatiotemporal behavior are clarified, and the revealed interval allows a realistic assessment of the quality of predictions and thus of associated algorithms. Additionally, in order to provide further insight into the practical use of the dataset, the relationship between the predictability and the check-in frequencies are investigated from three different perspectives. It was found that the individual perspective provides no significant correlations between the predictability and the check-in frequency. In contrast, the same two quantities are found to be negatively correlated from temporal and spatial perspectives. Our study further indicates that the heavily frequented contexts and some extraordinary geographic features such as airports could be good starting points for effective improvements of prediction algorithms. In general, this research provides novel knowledge regarding the nature of the LBSN dataset and practical insights for a more reasonable utilization of the dataset. Numéro de notice : A2018-349 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-016-0279-5 Date de publication en ligne : 25/11/2016 En ligne : https://doi.org/10.1007/s10707-016-0279-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90758 [article]

SAR 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5993 - 6006 Titre : SAR image change detection based on correlation kernel and multistage extreme learning machine Type de document : Article/Communication Auteurs : Lu Jia, Auteur ; Ming Li, Auteur ; Peng Zhang, Auteur ; Yan Wu, Auteur Année de publication : 2016 Article en page(s) : pp 5993 - 6006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] appariement d'images [Termes IGN] apprentissage automatique [Termes IGN] détection de changement [Termes IGN] détection de contours [Termes IGN] image radar moirée [Termes IGN] méthode fondée sur le noyau [Termes IGN] séparateur à vaste marge Résumé : (auteur) Designing a kernel function with good discriminating ability and a highly application-adaptive kernelized classifier is the key of many kernel methods. However, not many kernel functions combining directly the bitemporal images' information are designed specifically for change detection tasks. In addition, extreme learning machine (ELM) has not found wide applications in change detection tasks, even though it is a potential kernel method possessing outstanding approximation and generalization capabilities as well as great classification accuracy and efficiency. Therefore, an approach relying on a difference correlation kernel (DCK) and a multistage ELM (MS-ELM) is proposed in this paper for synthetic aperture radar (SAR) image change detection. First, a DCK function is constructed specifically for change detection by measuring the “distance” between any two pixels. The DCK function depicts the cross-time similarities between couples of bitemporal image patches at any cyclic shifts with a kernel correlation operation and the high-order spatial distances between two differently located pixels with an algebraic subtraction. The DCK function possesses strong noise immunity and good identification of changed areas simultaneously. Second, an MS-ELM classifier is constructed for obtaining the change detection result. In MS-ELM, the hidden nodes and weights between the hidden and output layers are updated stage by stage by improving the kernel functions that compose them. Each stage of the MS-ELM is a standard kernel-ELM, and the DCK function is utilized in the first stage. The regenerative kernel functions incorporate the output spatial-neighborhood information of the previous stage for enhancing remarkably the MS-ELM's discriminating ability and noise resistance. The converged result at the last stage of MS-ELM is the final change detection result. Experiments on real SAR image change detection demonstrate the effectiveness of the DCK function and the MS-ELM algorithm, particularly its good identification of changed areas and strong robustness against noise in SAR images. Numéro de notice : A2016-865 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2578438 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2578438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82901 [article]