[n° ou bulletin]
[n° ou bulletin]
|
Réservation
Réserver ce documentExemplaires (1)
Code-barres | Cote | Support | Localisation | Section | Disponibilité |
---|---|---|---|---|---|
079-2018011 | RAB | Revue | Centre de documentation | En réserve L003 | Disponible |
Dépouillements
Ajouter le résultat dans votre panierUnveiling movement uncertainty for robust trajectory similarity analysis / Andre Salvaro Furtado in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Unveiling movement uncertainty for robust trajectory similarity analysis Type de document : Article/Communication Auteurs : Andre Salvaro Furtado, Auteur ; Luis Otavio Alvares, Auteur ; Nikos Pelekis, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 140 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] distance
[Termes IGN] données localisées
[Termes IGN] incertitude des données
[Termes IGN] mesure de similitude multidimensionnelle
[Termes IGN] méthode robuste
[Termes IGN] trace GPS
[Termes IGN] trajet (mobilité)Résumé : (Auteur) Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article, we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work. Numéro de notice : A2018-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1372763 En ligne : https://doi.org/10.1080/13658816.2017.1372763 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89175
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 140 - 168[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible Leveraging correlation across space and time to interpolate geophysical data via CoKriging / Sonja Pravilovic in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Leveraging correlation across space and time to interpolate geophysical data via CoKriging Type de document : Article/Communication Auteurs : Sonja Pravilovic, Auteur ; Annalisa Appice, Auteur ; Donato Malerba, Auteur Année de publication : 2018 Article en page(s) : pp 191 - 212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage automatique
[Termes IGN] corrélation
[Termes IGN] données spatiotemporelles
[Termes IGN] interpolation
[Termes IGN] krigeageRésumé : (Auteur) Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time). Numéro de notice : A2018-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1381338 En ligne : https://doi.org/10.1080/13658816.2017.1381338 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89176
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 191 - 212[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible Spatial subdivision of complex indoor environments for 3D indoor navigation / Abdoulaye A. Diakité in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Spatial subdivision of complex indoor environments for 3D indoor navigation Type de document : Article/Communication Auteurs : Abdoulaye A. Diakité, Auteur ; Sisi Zlatanova, Auteur Année de publication : 2018 Article en page(s) : pp 213 - 235 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] indoorGML
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] patrimoine mobilier
[Termes IGN] positionnement en intérieur
[Termes IGN] sous-espaceRésumé : (Auteur) As we realize that we spend most of our time in increasingly complex indoor environments, applications to assist indoor activities (e.g. guidance) have gained a lot of attention in the recent years. The advances in ubiquitous computing made possible the development of several spatial models intending to support context-aware and fine-grained indoor navigation systems. However, the available models often rely on simplified representations (e.g. 2D plans) and ignore the indoor features (e.g. furniture), thereby missing to reflect the complexity of the indoor environment. In this paper, we introduce the Flexible Space Subdivision framework (FSS) that allows to automatically identify the spaces that can be used for indoor navigation purpose. We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions. The framework can consider any 3D indoor configuration, the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion (e.g. walking, flying, etc.). It relies on input 3D models with geometric, semantic and topological information and identifies a set of subspaces with dedicated properties. We assess the framework against criteria defined in previous researches and we provide an example. Numéro de notice : A2018-025 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1376066 En ligne : https://doi.org/10.1080/13658816.2017.1376066 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89177
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 213 - 235[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation / Qi Zhou in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation Type de document : Article/Communication Auteurs : Qi Zhou, Auteur Année de publication : 2018 Article en page(s) : pp 257 - 281 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] densité du bâti
[Termes IGN] exhaustivité des données
[Termes IGN] OpenStreetMap
[Termes IGN] qualité des données
[Termes IGN] résidu
[Termes IGN] zone urbaineRésumé : (Auteur) OpenStreetMap (OSM) is a free spatial data source based on crowd sourced data. Although the OSM data have a range of applications, such as generating 3D models, and routing and navigation, quality issues are still significant concerns when using the data. Several studies have undertaken quality assessments by comparing OSM data with reference data. However, reference data are not always available due to high costs or licensing restrictions, and very few studies have quantitatively estimated the quality of OSM data under conditions where the corresponding reference data are not available. This study proposed the use of a building density (or building coverage ratio) indicator as a proxy, and designed a series of experiments involving different study areas to quantitatively explore the relationship between building density and building completeness for OSM data in urban areas. The residuals (estimated building completeness and reference building completeness) were also analyzed. Two main results were found from the experiments. (1) There was an approximate linear relationship between building density and building completeness in the OSM data. More precisely, the building completeness of OSM data was approximately 3.4–4 times the building density of OSM data. (2) Approximately 70–80% of the absolute residuals were smaller than 10%, and 80–90% of them were smaller than 20%. This shows that, in most cases, estimated building completeness was close to the corresponding reference building completeness. Therefore, we concluded that the building density indicator is a potential proxy for the quantitative completeness estimation of OSM building data in urban areas. The limitations of using this indicator were also addressed. Numéro de notice : A2018-026 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1395883 En ligne : https://doi.org/10.1080/13658816.2017.1395883 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89178
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 257 - 281[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible Toponym matching through deep neural networks / Rui Santos in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Toponym matching through deep neural networks Type de document : Article/Communication Auteurs : Rui Santos, Auteur ; Patricia Murrieta-Flores, Auteur ; Pavel Calado, Auteur ; Bruno Martins, Auteur Année de publication : 2018 Article en page(s) : pp 324 - 348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] appariement
[Termes IGN] apprentissage profond
[Termes IGN] recherche d'information géographique
[Termes IGN] répertoire toponymique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] similitude sémantique
[Termes IGN] toponyme
[Termes IGN] traitement de données localiséesRésumé : (Auteur) Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics. Numéro de notice : A2018-027 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1390119 En ligne : https://doi.org/10.1080/13658816.2017.1390119 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89179
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 324 - 348[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible