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GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science / Jiaxin Du in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 873 - 897 Titre : GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science Type de document : Article/Communication Auteurs : Jiaxin Du, Auteur ; Shaohua Wang, Auteur ; Xinyue Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 873 - 897 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] exploration de données [Termes IGN] ingénierie des connaissances [Termes IGN] ontologie [Termes IGN] recherche d'information géographique [Termes IGN] réseau sémantique [Termes IGN] traitement du langage naturel Résumé : (auteur) An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes. Numéro de notice : A2022-341 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2005795 Date de publication en ligne : 26/11/2021 En ligne : https://doi.org/10.1080/13658816.2021.2005795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100515 [article]

HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1012 - 1036 Titre : HiPerMovelets: high-performance movelet extraction for trajectory classification Type de document : Article/Communication Auteurs : Tarlis Tortelli Portela, Auteur ; Jonata Tyska Carvalho, Auteur ; Vania Bogorny, Auteur Année de publication : 2022 Article en page(s) : pp 1012 - 1036 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification [Termes IGN] exploration de données géographiques [Termes IGN] jeu de données localisées [Termes IGN] trace numérique [Termes IGN] trajet (mobilité) Résumé : (auteur) In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories. Numéro de notice : A2022-332 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2021.2018593 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1080/13658816.2021.2018593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100608 [article]

Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 720 - 748 Titre : Discovering co-location patterns in multivariate spatial flow data Type de document : Article/Communication Auteurs : Jiannan Cai, Auteur ; Mei-Po Kwan, Auteur Année de publication : 2022 Article en page(s) : pp 720 - 748 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de groupement [Termes IGN] analyse univariée [Termes IGN] autocorrélation spatiale [Termes IGN] Chicago (Illinois) [Termes IGN] co-positionnement [Termes IGN] données de flux [Termes IGN] données socio-économiques [Termes IGN] dynamique spatiale [Termes IGN] enquête [Termes IGN] exploration de données géographiques [Termes IGN] migration pendulaire [Termes IGN] origine - destination [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. Numéro de notice : A2022-256 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1980217 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1980217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100229 [article]

Exploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)  

Public [article]  inComputers and graphics > vol 103 (April 2022) . - pp 140 - 152 Titre : Exploring scientific literature by textual and image content using DRIFT Type de document : Article/Communication Auteurs : Ximena Pocco, Auteur ; Tiago da Silva, Auteur ; Jorge Poco, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse visuelle [Termes IGN] bibliothèque numérique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corpus [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] recherche scientifique [Termes IGN] similitude sémantique Résumé : (auteur) Digital libraries represent the most valuable resource for storing, querying, and retrieving scientific literature. Traditionally, the reader/analyst aims to compose a set of articles based on keywords, according to his/her preferences, and manually inspect the resulting list of documents. Except for the articles which share citations or common keywords, the results retrieved will be limited to those which fulfill a syntactic match. Besides, if instead of having an article as a reference, the user has an image, the process of finding and exploring articles with similar content becomes infeasible. This paper proposes a visual analytic methodology for exploring and analyzing scientific document collections that consider both textual and image content. The proposed technique relies on combining multiple Content-Based Image Retrieval (CBIR) components and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Moreover, we extend its analytical capabilities with visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We evidence the effectiveness of our methodology through three case studies and a user evaluation, which attest to its usefulness during the process of scientific collections exploration. Numéro de notice : A2022-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cag.2022.02.005 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.1016/j.cag.2022.02.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100332 [article]

Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 839 - 866 Titre : Spatially oriented convolutional neural network for spatial relation extraction from natural language texts Type de document : Article/Communication Auteurs : Qinjun Qiu, Auteur ; Zhong Xie, Auteur ; Kai Ma, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 839 - 866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] appariement sémantique [Termes IGN] apprentissage dirigé [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] exploration de données [Termes IGN] langage naturel (informatique) [Termes IGN] proximité sémantique [Termes IGN] relation spatiale [Termes IGN] relation topologique [Termes IGN] site wiki [Termes IGN] spatial metrics [Termes IGN] système à base de connaissances Résumé : (auteur) Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task in several practical applications. Current state-of-the-art methods rely on rule-based metrics, either those specifically developed for extracting spatial relations or those integrated in methods that combine multiple metrics. However, these methods all rely on developed rules and do not effectively capture the characteristics of natural language spatial relations because the descriptions may be heterogeneous and vague and may be context sparse. In this article, we present a spatially oriented piecewise convolutional neural network (SP-CNN) that is specifically designed with these linguistic issues in mind. Our method extends a general piecewise convolutional neural network with a set of improvements designed to tackle the task of spatial relation extraction. We also propose an automated workflow for generating training datasets by integrating new sentences with those in a knowledge base, based on string similarity and semantic similarity, and then transforming the sentences into training data. We exploit a spatially oriented channel that uses prior human knowledge to automatically match words and understand the linguistic clues to spatial relations, finally leading to an extraction decision. We present both the qualitative and quantitative performance of the proposed methodology using a large dataset collected from Wikipedia. The experimental results demonstrate that the SP-CNN, with its supervised machine learning, can significantly outperform current state-of-the-art methods on constructed datasets. Numéro de notice : A2022-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12887 Date de publication en ligne : 27/12/2021 En ligne : https://doi.org/10.1111/tgis.12887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100584 [article]

Early warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)  

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A benchmark of named entity recognition approaches in historical documents : application to 19th century French directories / Nathalie Abadie (2022)  

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CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

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Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

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Effective triplet mining improves training of multi-scale pooled CNN for image retrieval / Federico Vaccaro in Machine Vision and Applications, vol 33 n° 1 (January 2022)  

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A prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

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Schematizing crossroads from abstract textual descriptions / Jean-Marie Favreau (2022)  

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Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

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La photogrammétrie appliquée au récolement des réseaux enterrés : retour d’expérience d’une méthode industrialisée / Jérôme Leroux in XYZ, n° 169 (décembre 2021)

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A quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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