Geoinformatica . vol 20 n° 4Mention de date : October - December 2016 Paru le : 01/10/2016 |
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est un bulletin de Geomatica / Canadian institute of geomatics = Association canadienne des sciences géomatiques (Canada) (1993 -)
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Dépouillements
Ajouter le résultat dans votre panierDeveloping a web-based system for supervised classification of remote sensing images / Ziheng Sun in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : Developing a web-based system for supervised classification of remote sensing images Type de document : Article/Communication Auteurs : Ziheng Sun, Auteur ; H. Fang, Auteur ; Liping Di, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 629 - 649 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] application web
[Termes IGN] classification automatique
[Termes IGN] classification dirigéeRésumé : (Auteur) Web-based image classification systems aim to provide users with an easy access to image classification function. The existing work mainly focuses on web-based unsupervised classification systems. This paper proposes a web-based supervised classification system framework which includes three modules: client, servlet and service. It comprehensively describes how to combine the procedures of supervised classification into the development of a web system. A series of methods are presented to realize the modules respectively. A prototype system of the framework is also implemented and a number of remote sensing (RS) images are tested on it. Experiment results show that the prototype is capable of accomplishing supervised classification of RS images on the Web. If appropriate algorithms and parameter values are used, the results of the web-based solution could be as accurate as the results of traditional desktop-based systems. This paper lays the foundation on both theoretical and practical aspects for the future development of operational web-based supervised classification systems. Numéro de notice : A2016-812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0252-3 En ligne : http://dx.doi.org/10.1007/s10707-016-0252-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82612
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 629 - 649[article]On 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)
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Titre : On discovering co-location patterns in datasets : a case study of pollutants and child cancers Type de document : Article/Communication Auteurs : Jundong Li, Auteur ; Aibek Adilmagambetov, Auteur ; Mohomed Shazan Mohomed Jabbar, Auteur Année de publication : 2016 Article en page(s) : pp 651 - 692 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme de tri
[Termes IGN] analyse spatiale
[Termes IGN] co-positionnement
[Termes IGN] enfant
[Termes IGN] exploration de données géographiques
[Termes IGN] polluant
[Termes IGN] santé
[Termes IGN] test statistiqueRésumé : (Auteur) We intend to identify relationships between cancer cases and pollutant emissions by proposing a novel co-location mining algorithm. In this context, we specifically attempt to understand whether there is a relationship between the location of a child diagnosed with cancer with any chemical combinations emitted from various facilities in that particular location. Co-location pattern mining intends to detect sets of spatial features frequently located in close proximity to each other. Most of the previous works in this domain are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds, and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. Our proposed approach is focused on a grid based transactionization? of the geographic space, and is designed to mine datasets with extended spatial objects. It is also capable of incorporating uncertainty of the existence of features to model real world scenarios more accurately. We eliminate the necessity of using a global threshold by introducing a statistical test to validate the significance of candidate co-location patterns and rules. Experiments on both synthetic and real datasets reveal that our algorithm can detect a considerable amount of statistically significant co-location patterns. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases. Numéro de notice : A2016-813 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0254-1 En ligne : http://dx.doi.org/10.1007/s10707-016-0254-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82614
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 651 - 692[article]A probabilistic approach to detect mixed periodic patterns from moving object data / Jun Li in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : A probabilistic approach to detect mixed periodic patterns from moving object data Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Jingjing Wang, Auteur ; Junfei Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 715 - 739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] comportement
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] estimation par noyau
[Termes IGN] objet mobile
[Termes IGN] séquence d'images
[Termes IGN] variable aléatoireRésumé : (Auteur) The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects. Numéro de notice : A2016-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0261-2 En ligne : http://dx.doi.org/10.1007/s10707-016-0261-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82615
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 715 - 739[article]Automatic targeted-domain spatiotemporal event detection in twitter / Ting Hua in Geoinformatica, vol 20 n° 4 (October - December 2016)
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Titre : Automatic targeted-domain spatiotemporal event detection in twitter Type de document : Article/Communication Auteurs : Ting Hua, Auteur ; Feng Chen, Auteur ; Liang Zhao, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 765 - 795 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] positionnement automatique
[Termes IGN] TwitterRésumé : (Auteur) Twitter has become an important data source for detecting events, especially tracking detailed information for events of a specific domain. Previous studies on targeted-domain Twitter information extraction have used supervised learning techniques to identify domain-related tweets, however, the need for extensive manual labeling makes these supervised systems extremely expensive to build and maintain. What’s more, most of these existing work fail to consider spatiotemporal factors, which are essential attributes of target-domain events. In this paper, we propose a semi-supervised method for Automatical Targeted-domain Spatiotemporal Event Detection (ATSED) in Twitter. Given a targeted domain, ATSED first learns tweet labels from historical data, and then detects on-going events from real-time Twitter data streams. Specifically, an efficient label generation algorithm is proposed to automatically recognize tweet labels from domain-related news articles, a customized classifier is created for Twitter data analysis by utilizing tweets’ distinguishing features, and a novel multinomial spatial-scan model is provided to identify geographical locations for detected events. Experiments on 305 million tweets demonstrated the effectiveness of this new approach. Numéro de notice : A2016-815 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1007/s10707-016-0263-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0263-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82616
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 765 - 795[article]Mining spatiotemporal co-occurrence patterns in non-relational databases / Berkay Aydin in Geoinformatica, vol 20 n° 4 (October - December 2016)
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Titre : Mining spatiotemporal co-occurrence patterns in non-relational databases Type de document : Article/Communication Auteurs : Berkay Aydin, Auteur ; Vijay Akkineni, Auteur ; Rafal Angryk, Auteur Année de publication : 2016 Article en page(s) : pp 801 - 828 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données répartie
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données géographiquesRésumé : (Auteur) Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining. Numéro de notice : A2016-816 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0255-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0255-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82618
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 801 - 828[article]The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing Type de document : Article/Communication Auteurs : Lihong Su, Auteur ; Y. Huang, Auteur ; James Gibeaut, Auteur ; Longzhuang Li, Auteur Année de publication : 2016 Article en page(s) : pp 859 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] distorsion d'image
[Termes IGN] géoréférencement
[Termes IGN] processeur graphiqueRésumé : (Auteur) Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation. Numéro de notice : A2016-817 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0253-2 En ligne : http://dx.doi.org/10.1007/s10707-016-0253-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82621
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 859 - 878[article]